Get Up and LEARN

Transform your business through people

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About Get Up and LEARN
Get Up and Learn is a training company unlike any you've seen before! We are a new economy business with global access to the best training content available, and local relationships with you, our customers. We offer one and two-day soft-skills training courses in lea...
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$25 - $49/hr
2 - 9
2009
Canada
Get Up and LEARN
Transform your business through people
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38 Questions
With technological advances and digital progress, now there are distinct tactics possible to attract and retain customers. One such technological trend in the industry currently is Machine Learning. This technology makes mobile platforms more user-friendly, enhances the customer experience, maintains customer loyalty, and helps in developing consistent omnichannel skills. Let's have a look at some of the best use-cases of machine learning in mobile apps: 1. Customized Experience Machine learning can assist you with the continual learning process. With the help of Machine Learning, you can identify users based on their interests and further design the appearance of your app based on the user’s requirements. Moreover, algorithms can examine multiple sources of information ranging from social media activities to credit ratings and based on the analysis pop recommendations right onto user’s devices.  You can have an in-depth knowledge of your customers interests with the help of machine learning, such as: Who are your customers? What exactly do they require? What services are they looking for? What hobbies, preferences, and pain points they possess? What words they’re using to describe your products? Based on the aggregate data collected from the above questions, you can distinguish and structure your customers. You can also find a specific approach to connect each customer group by accordingly adapting the tone of your content. Machine learning enables you to provide your users with the most relevant and enticing content. This will ensure users remain engaged with your app. For instance, in 2013, Netflix app launched the ‘House of Cards’ and used big data to choose the main character (Kevin Spacey) and one of the executive producers (David Fincher) for this show. This depicts the perfect use case of machine learning in various aspects of mobile apps. 2. Sophisticated Research Another compelling use case of machine learning solutions is that it enables you to deliver excellent and more contextual outcomes. It makes the process of searching more intuitive and easily accessible to your customers. Machine learning algorithms analyze customer’s queries and prioritize the results based on the specific requirements of a particular customer. With the help of a mobile app, you can collect all available data about your customers like search histories, and typical actions. You can further use this data combined with behavioural data and search requests. Based on the results received, you can show customized results of your product listings to your visitors. For instance, Reddit app is implementing machine learning to improvise search performance for hundreds of millions of community members. 3. Foreseen User Experience Another use case of machine learning in mobile apps is that it helps marketers understand user’s preferences and behaviour patterns by scrutinizing various data such as: Age Gender Location Search requests Frequency of app usage By collecting and analyzing all the data mentioned above, you can improve the effectiveness of your app and your marketing efforts. Suppose you analyze that the number of females using your app is more than that of males and most of them are under 40 years of age. Based on this information, you can design your net campaign to either attract a male audience or target your entire marketing campaign at women under 40. Machine learning also helps in curating personalized recommendations that enhance customer engagement and time spent on your app. For instance, Amazon’s app suggestion mechanism is designed in such a manner that while customers are browsing, a machine learning algorithm constantly analyzes their preferences. 4. Significant Advertisements The success of a product or an app depends upon the significant advertisements. Consumers are focusing more on the products that are useful as well as contextually relevant and personalized. Machine learning aids an organization’s target display advertisements and personalized messaging more accurately. Such accurate results help app developers to generate ads focusing on data about each customer’s unique interests and buying propensities. By implementing machine learning, you can predict how a specific customer will react to a given promotion so you can display only specific ads that interest them. This helps you save time and capital and also lets you focus on improving the brand’s reputation. For instance, Coca-Cola pays close attention to how its products are represented across social media. The company implements image recognition technology to identify when people have posted images of its products or those of competitors on Facebook, Instagram, and Twitter. This information helps Coca-Cola to understand what situations prompt customers to talk about its brand. Ads designed in such a way have higher chances of being clicked on than ads created using other methods of targeted advertising. 5. Enhanced Security Machine learning can streamline and secure the app authentication process. Video, audio, and voice recognition features allow customers to authenticate using their biometric data such as face or fingerprint. Machine learning also aids you in determining access rights for your customers as well. Apps such as Zoom Login and BioID utilize machine learning and enable easy log in to other websites and apps with ultra-secure, selfie-style face authentication. For instance, big companies like Uber leverage machine learning for implementing face detection technology to address any fraudulent behavior. With the help of this technology, they can identify fraudsters who use stolen credit cards. Some of the big names in the banking sector and financial industries are also implementing machine learning algorithms to scrutinize customer’s previous transactions, social media activities, and borrowing history. With machine learning, following distinct features related to security can be accessed: Image recognition Shipping cost estimation Product tagging automation Wallet management Logistics optimization Business intelligence 6. Profound User Engagement Machine learning tools enable you to offer robust customer support, a wide range of features, and entertainment that binds your customers to your app daily. Certain profound features of machine learning can help you keep your customers engaged. Sufficient Support Amazon and Facebook app utilize a machine-learning algorithm to engage users with intelligent request handling. Machine learning systems can quickly analyze vast sets of data and make quick decisions. Various companies use machine learning to design conversational UX, or virtual assistants known as AI chatbots that assist in making calls, writing long emails, or waiting on the phone until somebody responds. You can even utilize machine learning algorithms to send smart notifications and voice notifications about the current deals going on. By performing these tasks, this technology helps your brand to become more proactive and keep your customers engaged. In short Machine learning tools enables you to offer solid customer support, a wide range of features, and entertainment that binds your customers to your app on a daily basis. Other Effective Use-Cases: Machine learning intelligence enables you to promote your app with a built-in translator. These processes help international customers communicate within your app without the involvement of third-party online translators. Airbnb app uses Cloud Translation API to translate listings, reviews, and conversations between its users, as almost 60 percent of its users use the app in different languages. Wrapping Up: Machine learning technology can authorize your mobile app with an efficient customization engine, cutting-edge search mechanisms, quick and reliable authentication, and fraud protection. OpenXcell Technolabs provides excellent mobile app development services. Leveraging the trending machine learning technology, the company delivers app solutions that are Emotionally Intelligent, Free to Explore, Pre-Trained, and comes with Autonomous Reasoning. Apart from machine learning, the company encompasses skilled, innovative talents for Cloud Computing, IoT, Artificial Intelligence, and more to upscale your customer base for mobile app development. OpenXcell Technolabs holds expertise in developing mobile apps that implement machine learning. This technology can help generate proactive customer interactions, monitor data to get insights, and boost lead generation metrics. If you want to implement machine learning algorithms to your mobile app, OpenXcell Technolabs is the one-stop destination for all such requirements.
With technological advances and digital progress, now there are distinct tactics possible to attract and retain customers. One such technological trend in the industry currently is Machine Learning. This technology makes mobile platforms more user-friendly, enhances the customer experience, maintains customer loyalty, and helps in developing consistent omnichannel skills. Let's have a look at some of the best use-cases of machine learning in mobile apps: 1. Customized Experience Machine learning can assist you with the continual learning process. With the help of Machine Learning, you can identify users based on their interests and further design the appearance of your app based on the user’s requirements. Moreover, algorithms can examine multiple sources of information ranging from social media activities to credit ratings and based on the analysis pop recommendations right onto user’s devices.  You can have an in-depth knowledge of your customers interests with the help of machine learning, such as: Who are your customers? What exactly do they require? What services are they looking for? What hobbies, preferences, and pain points they possess? What words they’re using to describe your products? Based on the aggregate data collected from the above questions, you can distinguish and structure your customers. You can also find a specific approach to connect each customer group by accordingly adapting the tone of your content. Machine learning enables you to provide your users with the most relevant and enticing content. This will ensure users remain engaged with your app. For instance, in 2013, Netflix app launched the ‘House of Cards’ and used big data to choose the main character (Kevin Spacey) and one of the executive producers (David Fincher) for this show. This depicts the perfect use case of machine learning in various aspects of mobile apps. 2. Sophisticated Research Another compelling use case of machine learning solutions is that it enables you to deliver excellent and more contextual outcomes. It makes the process of searching more intuitive and easily accessible to your customers. Machine learning algorithms analyze customer’s queries and prioritize the results based on the specific requirements of a particular customer. With the help of a mobile app, you can collect all available data about your customers like search histories, and typical actions. You can further use this data combined with behavioural data and search requests. Based on the results received, you can show customized results of your product listings to your visitors. For instance, Reddit app is implementing machine learning to improvise search performance for hundreds of millions of community members. 3. Foreseen User Experience Another use case of machine learning in mobile apps is that it helps marketers understand user’s preferences and behaviour patterns by scrutinizing various data such as: Age Gender Location Search requests Frequency of app usage By collecting and analyzing all the data mentioned above, you can improve the effectiveness of your app and your marketing efforts. Suppose you analyze that the number of females using your app is more than that of males and most of them are under 40 years of age. Based on this information, you can design your net campaign to either attract a male audience or target your entire marketing campaign at women under 40. Machine learning also helps in curating personalized recommendations that enhance customer engagement and time spent on your app. For instance, Amazon’s app suggestion mechanism is designed in such a manner that while customers are browsing, a machine learning algorithm constantly analyzes their preferences. 4. Significant Advertisements The success of a product or an app depends upon the significant advertisements. Consumers are focusing more on the products that are useful as well as contextually relevant and personalized. Machine learning aids an organization’s target display advertisements and personalized messaging more accurately. Such accurate results help app developers to generate ads focusing on data about each customer’s unique interests and buying propensities. By implementing machine learning, you can predict how a specific customer will react to a given promotion so you can display only specific ads that interest them. This helps you save time and capital and also lets you focus on improving the brand’s reputation. For instance, Coca-Cola pays close attention to how its products are represented across social media. The company implements image recognition technology to identify when people have posted images of its products or those of competitors on Facebook, Instagram, and Twitter. This information helps Coca-Cola to understand what situations prompt customers to talk about its brand. Ads designed in such a way have higher chances of being clicked on than ads created using other methods of targeted advertising. 5. Enhanced Security Machine learning can streamline and secure the app authentication process. Video, audio, and voice recognition features allow customers to authenticate using their biometric data such as face or fingerprint. Machine learning also aids you in determining access rights for your customers as well. Apps such as Zoom Login and BioID utilize machine learning and enable easy log in to other websites and apps with ultra-secure, selfie-style face authentication. For instance, big companies like Uber leverage machine learning for implementing face detection technology to address any fraudulent behavior. With the help of this technology, they can identify fraudsters who use stolen credit cards. Some of the big names in the banking sector and financial industries are also implementing machine learning algorithms to scrutinize customer’s previous transactions, social media activities, and borrowing history. With machine learning, following distinct features related to security can be accessed: Image recognition Shipping cost estimation Product tagging automation Wallet management Logistics optimization Business intelligence 6. Profound User Engagement Machine learning tools enable you to offer robust customer support, a wide range of features, and entertainment that binds your customers to your app daily. Certain profound features of machine learning can help you keep your customers engaged. Sufficient Support Amazon and Facebook app utilize a machine-learning algorithm to engage users with intelligent request handling. Machine learning systems can quickly analyze vast sets of data and make quick decisions. Various companies use machine learning to design conversational UX, or virtual assistants known as AI chatbots that assist in making calls, writing long emails, or waiting on the phone until somebody responds. You can even utilize machine learning algorithms to send smart notifications and voice notifications about the current deals going on. By performing these tasks, this technology helps your brand to become more proactive and keep your customers engaged. In short Machine learning tools enables you to offer solid customer support, a wide range of features, and entertainment that binds your customers to your app on a daily basis. Other Effective Use-Cases: Machine learning intelligence enables you to promote your app with a built-in translator. These processes help international customers communicate within your app without the involvement of third-party online translators. Airbnb app uses Cloud Translation API to translate listings, reviews, and conversations between its users, as almost 60 percent of its users use the app in different languages. Wrapping Up: Machine learning technology can authorize your mobile app with an efficient customization engine, cutting-edge search mechanisms, quick and reliable authentication, and fraud protection. OpenXcell Technolabs provides excellent mobile app development services. Leveraging the trending machine learning technology, the company delivers app solutions that are Emotionally Intelligent, Free to Explore, Pre-Trained, and comes with Autonomous Reasoning. Apart from machine learning, the company encompasses skilled, innovative talents for Cloud Computing, IoT, Artificial Intelligence, and more to upscale your customer base for mobile app development. OpenXcell Technolabs holds expertise in developing mobile apps that implement machine learning. This technology can help generate proactive customer interactions, monitor data to get insights, and boost lead generation metrics. If you want to implement machine learning algorithms to your mobile app, OpenXcell Technolabs is the one-stop destination for all such requirements.

With technological advances and digital progress, now there are distinct tactics possible to attract and retain customers. One such technological trend in the industry currently is Machine Learning. This technology makes mobile platforms more user-friendly, enhances the customer experience, maintains customer loyalty, and helps in developing consistent omnichannel skills.

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Let's have a look at some of the best use-cases of machine learning in mobile apps:

1. Customized Experience

Machine learning can assist you with the continual learning process. With the help of Machine Learning, you can identify users based on their interests and further design the appearance of your app based on the user’s requirements. Moreover, algorithms can examine multiple sources of information ranging from social media activities to credit ratings and based on the analysis pop recommendations right onto user’s devices. 

You can have an in-depth knowledge of your customers interests with the help of machine learning, such as:

  • Who are your customers?
  • What exactly do they require?
  • What services are they looking for?
  • What hobbies, preferences, and pain points they possess?
  • What words they’re using to describe your products?

Based on the aggregate data collected from the above questions, you can distinguish and structure your customers. You can also find a specific approach to connect each customer group by accordingly adapting the tone of your content. Machine learning enables you to provide your users with the most relevant and enticing content. This will ensure users remain engaged with your app.

For instance, in 2013, Netflix app launched the ‘House of Cards’ and used big data to choose the main character (Kevin Spacey) and one of the executive producers (David Fincher) for this show. This depicts the perfect use case of machine learning in various aspects of mobile apps.

2. Sophisticated Research

Another compelling use case of machine learning solutions is that it enables you to deliver excellent and more contextual outcomes. It makes the process of searching more intuitive and easily accessible to your customers. Machine learning algorithms analyze customer’s queries and prioritize the results based on the specific requirements of a particular customer.

With the help of a mobile app, you can collect all available data about your customers like search histories, and typical actions. You can further use this data combined with behavioural data and search requests. Based on the results received, you can show customized results of your product listings to your visitors.

For instance, Reddit app is implementing machine learning to improvise search performance for hundreds of millions of community members.

3. Foreseen User Experience

Another use case of machine learning in mobile apps is that it helps marketers understand user’s preferences and behaviour patterns by scrutinizing various data such as:

  • Age
  • Gender
  • Location
  • Search requests
  • Frequency of app usage

By collecting and analyzing all the data mentioned above, you can improve the effectiveness of your app and your marketing efforts. Suppose you analyze that the number of females using your app is more than that of males and most of them are under 40 years of age. Based on this information, you can design your net campaign to either attract a male audience or target your entire marketing campaign at women under 40.

Machine learning also helps in curating personalized recommendations that enhance customer engagement and time spent on your app.

For instance, Amazon’s app suggestion mechanism is designed in such a manner that while customers are browsing, a machine learning algorithm constantly analyzes their preferences.

4. Significant Advertisements

The success of a product or an app depends upon the significant advertisements. Consumers are focusing more on the products that are useful as well as contextually relevant and personalized. Machine learning aids an organization’s target display advertisements and personalized messaging more accurately. Such accurate results help app developers to generate ads focusing on data about each customer’s unique interests and buying propensities.

By implementing machine learning, you can predict how a specific customer will react to a given promotion so you can display only specific ads that interest them. This helps you save time and capital and also lets you focus on improving the brand’s reputation.

For instance, Coca-Cola pays close attention to how its products are represented across social media. The company implements image recognition technology to identify when people have posted images of its products or those of competitors on Facebook, Instagram, and Twitter. This information helps Coca-Cola to understand what situations prompt customers to talk about its brand. Ads designed in such a way have higher chances of being clicked on than ads created using other methods of targeted advertising.

5. Enhanced Security

Machine learning can streamline and secure the app authentication process. Video, audio, and voice recognition features allow customers to authenticate using their biometric data such as face or fingerprint. Machine learning also aids you in determining access rights for your customers as well.

Apps such as Zoom Login and BioID utilize machine learning and enable easy log in to other websites and apps with ultra-secure, selfie-style face authentication.

For instance, big companies like Uber leverage machine learning for implementing face detection technology to address any fraudulent behavior. With the help of this technology, they can identify fraudsters who use stolen credit cards.

Some of the big names in the banking sector and financial industries are also implementing machine learning algorithms to scrutinize customer’s previous transactions, social media activities, and borrowing history.

With machine learning, following distinct features related to security can be accessed:

  • Image recognition
  • Shipping cost estimation
  • Product tagging automation
  • Wallet management
  • Logistics optimization
  • Business intelligence

6. Profound User Engagement

Machine learning tools enable you to offer robust customer support, a wide range of features, and entertainment that binds your customers to your app daily. Certain profound features of machine learning can help you keep your customers engaged.

Sufficient Support

Amazon and Facebook app utilize a machine-learning algorithm to engage users with intelligent request handling. Machine learning systems can quickly analyze vast sets of data and make quick decisions.

Various companies use machine learning to design conversational UX, or virtual assistants known as AI chatbots that assist in making calls, writing long emails, or waiting on the phone until somebody responds. You can even utilize machine learning algorithms to send smart notifications and voice notifications about the current deals going on. By performing these tasks, this technology helps your brand to become more proactive and keep your customers engaged. In short Machine learning tools enables you to offer solid customer support, a wide range of features, and entertainment that binds your customers to your app on a daily basis.

Other Effective Use-Cases:

Machine learning intelligence enables you to promote your app with a built-in translator. These processes help international customers communicate within your app without the involvement of third-party online translators.

Airbnb app uses Cloud Translation API to translate listings, reviews, and conversations between its users, as almost 60 percent of its users use the app in different languages.

Wrapping Up:

Machine learning technology can authorize your mobile app with an efficient customization engine, cutting-edge search mechanisms, quick and reliable authentication, and fraud protection. OpenXcell Technolabs provides excellent mobile app development services. Leveraging the trending machine learning technology, the company delivers app solutions that are Emotionally Intelligent, Free to Explore, Pre-Trained, and comes with Autonomous Reasoning. Apart from machine learning, the company encompasses skilled, innovative talents for Cloud Computing, IoT, Artificial Intelligence, and more to upscale your customer base for mobile app development. OpenXcell Technolabs holds expertise in developing mobile apps that implement machine learning. This technology can help generate proactive customer interactions, monitor data to get insights, and boost lead generation metrics. If you want to implement machine learning algorithms to your mobile app, OpenXcell Technolabs is the one-stop destination for all such requirements.

Machine learning is a technology about teaching computers how to make decisions or predictions by learning from data. This process begins with feeding them high-quality data and then training the machines by creating various machine learning models using algorithms. But people are still confused about the exact ML algorithm and how to start learning it? So, I have tried to explain the basics of this technology and also the steps you can follow to become a full-fledged Machine Learning Engineer eventually.  How to Begin Machine Learning?  A rough roadmap is given below that would lead you to gain better insights on machine learning.  Step 1: Understand the Prerequisites  To start the process of machine learning, there are some prerequisites that you need to understand. These prerequisites consist of Linear Algebra, Multivariate Calculus, Statistics, and Python. You should have a basic knowledge of these techniques, to begin with, ML. Let’s understand these prerequisites better:   Learn Linear Algebra and Multivariate Calculus  Both Linear Algebra and Multivariate Calculus are crucial aspects of Machine Learning. However, the extent to which you require them relies on your role as a data scientist. If you concentrate on application substantial machine learning, then you will not be focusing much on maths as there are several standard libraries available. But if you want to emphasize on R&D, then mastering these techniques is essential.   Learn Statistics  Data is one of the essential aspects of Machine Learning, and it is believed that if you are an ML expert, most of your time will be spent collecting and clearing data. Statistics is a field that manages the collection, analysis, and presentation of the data. Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, and a few others are some of the crucial concepts in learning statistics.   Learn Python  You may skip Linear Algebra, Multivariate Calculus, and Statistics at times, but you can never skip Python. At present, Python is a widely used language for Machine Learning. Many Python libraries are specifically useful for AI and ML, such as Keras, TensorFlow, Scikit-Learn, and others.  Step 2: Learn Various ML Concepts  The second step after learning about the prerequisites is to start with the basics and then move further to the complicated theories. Some of the basic concepts in Machine Learning that you need to know about are: Terminologies of Machine Learning Get a clear understanding of different terminologies of Machine Learning like Model, Feature, Target (Label), Training & Prediction.   Types of Machine Learning  Supervised Learning - This learning is derived from a training dataset with labeled data using classification and regression models, and it continues until the required level of performance is achieved.  Unsupervised Learning - This learning is carried out using unlabelled data and then identifying the underlying structure in the data to learn about the data using factor and cluster analysis models.  Semi-supervised Learning - This learning utilizes unlabelled data like Unsupervised Learning with a small amount of labeled data, which increases the learning accuracy and is also more cost-effective.  Reinforcement Learning - This learning considers optimal actions through trial and error. Step 3: Practice Machine Learning  The most tedious part in ML is data collection, integration, cleaning, and preprocessing. It is crucial to practice generating high-quality data from tons of data available.  Studying different models and practicing on real datasets will help you in analyzing things under varied conditions.  Apart from the following steps, it is essential to understand how to analyze the results obtained by studying different models and regularization methods applied to various models.   Participate in Competitions  After understanding the basics, you must participate in various competitions, as doing this would make you proficient in ML. This will give you more confidence about your learnings as it will let you utilize your theoretical knowledge with practical implementation.  Last Words:  All the steps mentioned above would definitely help you get a clear understanding of how to learn Machine Learning. You can also upscale your skills and knowledge by working on different ML challenges. These challenges would eventually help you master ML skills.
Machine learning is a technology about teaching computers how to make decisions or predictions by learning from data. This process begins with feeding them high-quality data and then training the machines by creating various machine learning models using algorithms. But people are still confused about the exact ML algorithm and how to start learning it? So, I have tried to explain the basics of this technology and also the steps you can follow to become a full-fledged Machine Learning Engineer eventually.  How to Begin Machine Learning?  A rough roadmap is given below that would lead you to gain better insights on machine learning.  Step 1: Understand the Prerequisites  To start the process of machine learning, there are some prerequisites that you need to understand. These prerequisites consist of Linear Algebra, Multivariate Calculus, Statistics, and Python. You should have a basic knowledge of these techniques, to begin with, ML. Let’s understand these prerequisites better:   Learn Linear Algebra and Multivariate Calculus  Both Linear Algebra and Multivariate Calculus are crucial aspects of Machine Learning. However, the extent to which you require them relies on your role as a data scientist. If you concentrate on application substantial machine learning, then you will not be focusing much on maths as there are several standard libraries available. But if you want to emphasize on R&D, then mastering these techniques is essential.   Learn Statistics  Data is one of the essential aspects of Machine Learning, and it is believed that if you are an ML expert, most of your time will be spent collecting and clearing data. Statistics is a field that manages the collection, analysis, and presentation of the data. Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, and a few others are some of the crucial concepts in learning statistics.   Learn Python  You may skip Linear Algebra, Multivariate Calculus, and Statistics at times, but you can never skip Python. At present, Python is a widely used language for Machine Learning. Many Python libraries are specifically useful for AI and ML, such as Keras, TensorFlow, Scikit-Learn, and others.  Step 2: Learn Various ML Concepts  The second step after learning about the prerequisites is to start with the basics and then move further to the complicated theories. Some of the basic concepts in Machine Learning that you need to know about are: Terminologies of Machine Learning Get a clear understanding of different terminologies of Machine Learning like Model, Feature, Target (Label), Training & Prediction.   Types of Machine Learning  Supervised Learning - This learning is derived from a training dataset with labeled data using classification and regression models, and it continues until the required level of performance is achieved.  Unsupervised Learning - This learning is carried out using unlabelled data and then identifying the underlying structure in the data to learn about the data using factor and cluster analysis models.  Semi-supervised Learning - This learning utilizes unlabelled data like Unsupervised Learning with a small amount of labeled data, which increases the learning accuracy and is also more cost-effective.  Reinforcement Learning - This learning considers optimal actions through trial and error. Step 3: Practice Machine Learning  The most tedious part in ML is data collection, integration, cleaning, and preprocessing. It is crucial to practice generating high-quality data from tons of data available.  Studying different models and practicing on real datasets will help you in analyzing things under varied conditions.  Apart from the following steps, it is essential to understand how to analyze the results obtained by studying different models and regularization methods applied to various models.   Participate in Competitions  After understanding the basics, you must participate in various competitions, as doing this would make you proficient in ML. This will give you more confidence about your learnings as it will let you utilize your theoretical knowledge with practical implementation.  Last Words:  All the steps mentioned above would definitely help you get a clear understanding of how to learn Machine Learning. You can also upscale your skills and knowledge by working on different ML challenges. These challenges would eventually help you master ML skills.

Machine learning is a technology about teaching computers how to make decisions or predictions by learning from data. This process begins with feeding them high-quality data and then training the machines by creating various machine learning models using algorithms. But people are still confused about the exact ML algorithm and how to start learning it? So, I have tried to explain the basics of this technology and also the steps you can follow to become a full-fledged Machine Learning Engineer eventually.

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 How to Begin Machine Learning? 

A rough roadmap is given below that would lead you to gain better insights on machine learning. 

Step 1: Understand the Prerequisites 

To start the process of machine learning, there are some prerequisites that you need to understand. These prerequisites consist of Linear Algebra, Multivariate Calculus, Statistics, and Python. You should have a basic knowledge of these techniques, to begin with, ML. Let’s understand these prerequisites better:  

  • Learn Linear Algebra and Multivariate Calculus 

Both Linear Algebra and Multivariate Calculus are crucial aspects of Machine Learning. However, the extent to which you require them relies on your role as a data scientist. If you concentrate on application substantial machine learning, then you will not be focusing much on maths as there are several standard libraries available. But if you want to emphasize on R&D, then mastering these techniques is essential.  

  • Learn Statistics 

Data is one of the essential aspects of Machine Learning, and it is believed that if you are an ML expert, most of your time will be spent collecting and clearing data. Statistics is a field that manages the collection, analysis, and presentation of the data. Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, and a few others are some of the crucial concepts in learning statistics.  

  • Learn Python 

You may skip Linear Algebra, Multivariate Calculus, and Statistics at times, but you can never skip Python. At present, Python is a widely used language for Machine Learning. Many Python libraries are specifically useful for AI and ML, such as Keras, TensorFlow, Scikit-Learn, and others. 

Step 2: Learn Various ML Concepts 

The second step after learning about the prerequisites is to start with the basics and then move further to the complicated theories. Some of the basic concepts in Machine Learning that you need to know about are:

Terminologies of Machine Learning

Get a clear understanding of different terminologies of Machine Learning like Model, Feature, Target (Label), Training & Prediction.  

Types of Machine Learning 

  • Supervised Learning - This learning is derived from a training dataset with labeled data using classification and regression models, and it continues until the required level of performance is achieved. 
  • Unsupervised Learning - This learning is carried out using unlabelled data and then identifying the underlying structure in the data to learn about the data using factor and cluster analysis models. 
  • Semi-supervised Learning - This learning utilizes unlabelled data like Unsupervised Learning with a small amount of labeled data, which increases the learning accuracy and is also more cost-effective. 
  • Reinforcement Learning - This learning considers optimal actions through trial and error.

Step 3: Practice Machine Learning 

  • The most tedious part in ML is data collection, integration, cleaning, and preprocessing. It is crucial to practice generating high-quality data from tons of data available. 
  • Studying different models and practicing on real datasets will help you in analyzing things under varied conditions. 
  • Apart from the following steps, it is essential to understand how to analyze the results obtained by studying different models and regularization methods applied to various models.  

Participate in Competitions 

After understanding the basics, you must participate in various competitions, as doing this would make you proficient in ML. This will give you more confidence about your learnings as it will let you utilize your theoretical knowledge with practical implementation. 

Last Words: 

All the steps mentioned above would definitely help you get a clear understanding of how to learn Machine Learning. You can also upscale your skills and knowledge by working on different ML challenges. These challenges would eventually help you master ML skills.

Deep Learning and Machine Learning are subsets of Artificial Intelligence. These intelligent processes consist of learning, reasoning, and self-correction.     Artificial Intelligence is a vast advancement in the technology sector. It utilizes human intelligence and is implemented by machines.     The fig. below depicts a clear representation of the matter:       Before knowing the differences between deep learning and machine learning, it's important to understand both the terms precisely.  Deep Learning  Deep learning is a part of artificial intelligence that functions by imitating the workings of the human brain in data processing and creating patterns that can be effectively used in decision making.       Deep learning originated from artificial intelligence (AI) that features networks capable of learning unsupervised from data that is unstructured or unlabeled.     Machine Learning   Machine learning is a faction of artificial intelligence (AI) that offers systems the potential to understand and modify from experience without being required to explicitly programmed.         Machine learning emphasizes on the development of computer programs that can identify patterns, learn from data and can operate with minimum human help.     Major Differences between Deep Learning and Machine Learning:   The significant and critical difference between deep learning and machine learning is classified in the way data is presented to the system.          Listing out a few more differences to make you clear about the differences between deep learning and machine learning:       Conclusion: The most straightforward way for understanding the difference between machine learning and deep learning is to understand the fact that deep learning is machine learning. More specifically, deep learning is considered as an evolution of machine learning. It utilizes a programmable neural network that provides an opportunity for the machines to make accurate decisions without human intervention.
Deep Learning and Machine Learning are subsets of Artificial Intelligence. These intelligent processes consist of learning, reasoning, and self-correction.     Artificial Intelligence is a vast advancement in the technology sector. It utilizes human intelligence and is implemented by machines.     The fig. below depicts a clear representation of the matter:       Before knowing the differences between deep learning and machine learning, it's important to understand both the terms precisely.  Deep Learning  Deep learning is a part of artificial intelligence that functions by imitating the workings of the human brain in data processing and creating patterns that can be effectively used in decision making.       Deep learning originated from artificial intelligence (AI) that features networks capable of learning unsupervised from data that is unstructured or unlabeled.     Machine Learning   Machine learning is a faction of artificial intelligence (AI) that offers systems the potential to understand and modify from experience without being required to explicitly programmed.         Machine learning emphasizes on the development of computer programs that can identify patterns, learn from data and can operate with minimum human help.     Major Differences between Deep Learning and Machine Learning:   The significant and critical difference between deep learning and machine learning is classified in the way data is presented to the system.          Listing out a few more differences to make you clear about the differences between deep learning and machine learning:       Conclusion: The most straightforward way for understanding the difference between machine learning and deep learning is to understand the fact that deep learning is machine learning. More specifically, deep learning is considered as an evolution of machine learning. It utilizes a programmable neural network that provides an opportunity for the machines to make accurate decisions without human intervention.

Deep Learning and Machine Learning are subsets of Artificial Intelligence. These intelligent processes consist of learning, reasoning, and self-correction.    

Artificial Intelligence is a vast advancement in the technology sector. It utilizes human intelligence and is implemented by machines.    

The fig. below depicts a clear representation of the matter:    

 

Before knowing the differences between deep learning and machine learning, it's important to understand both the terms precisely. 

Deep Learning 

Deep learning is a part of artificial intelligence that functions by imitating the workings of the human brain in data processing and creating patterns that can be effectively used in decision making. 

 

  

Deep learning originated from artificial intelligence (AI) that features networks capable of learning unsupervised from data that is unstructured or unlabeled.    

Machine Learning  

Machine learning is a faction of artificial intelligence (AI) that offers systems the potential to understand and modify from experience without being required to explicitly programmed.    

 

 

Machine learning emphasizes on the development of computer programs that can identify patterns, learn from data and can operate with minimum human help.    

Major Differences between Deep Learning and Machine Learning:  

The significant and critical difference between deep learning and machine learning is classified in the way data is presented to the system.    

 

  

Listing out a few more differences to make you clear about the differences between deep learning and machine learning:    

 

Conclusion: The most straightforward way for understanding the difference between machine learning and deep learning is to understand the fact that deep learning is machine learning. More specifically, deep learning is considered as an evolution of machine learning. It utilizes a programmable neural network that provides an opportunity for the machines to make accurate decisions without human intervention.

Artificial intelligence is a simulation of the human brain in the best of its form. Their neural network is far quicker than human reflexes that could capture views even from a speeding train. Over the years, artificial intelligence has improved in its comprehensive skills and decision power by refining the accumulated data and analyzing them just as humans do.  It has been possible to wire electrical components to perform intellectual jobs. However, the data-scientist added one more tool to enhance AI capabilities called “Machine Learning.”  (christophm.github)   How is ML different from AI? - Machine learning is not separate from AI; it is a subset of artificial intelligence. In general, Machine Learning is the smarter version of AI that does not require to program over and again. Basically, in AI, the software behaves according to the specific instructions (spoon-feeding) or program. But in ML, you don’t prompt the machine how to behave; all you have to do is feed the data and rest work is done by ML. Unlike AI, ML does not need millions of codes, complex rules, and decision trees to perform any particular task but instead uses algorithms that adjust itself and improves. It trains itself to change the algorithm as per the data input and takes action accordingly.   You can resemble it with manual vs. automatic cars. In a manual car, you have to shift the gears manually whenever you want to increase/decrease the speed of the car, in automatic cars the system takes care of the gear-box, and all you need to do is to control the acceleration padel.   Example of Machine Learning- One of the best examples of Machine learning is “product recommendation,” you too may have experienced this while accessing a shopping app or website when a message pops out recommending the product based on your past buying history. To see what is the difference between them, see the image below.   ( Image source: quora)  There are a few more details that set them apart from each other.  Machine learning vs. Artificial Intelligence  Machine learning has equipped the business owner to achieve pinpoint accuracy in reading customer’s demand and design products accordingly. The ML model can be used to segment customers, spot anomalies, or forecast sales. With AutoML kicking in, these processes will get even better, and companies that were reluctant to adopting ML application due to lack of expertise will now embrace it with open hands. AutoML will eliminate the labor-intensive job of choosing and tuning the machine-learning models.
Artificial intelligence is a simulation of the human brain in the best of its form. Their neural network is far quicker than human reflexes that could capture views even from a speeding train. Over the years, artificial intelligence has improved in its comprehensive skills and decision power by refining the accumulated data and analyzing them just as humans do.  It has been possible to wire electrical components to perform intellectual jobs. However, the data-scientist added one more tool to enhance AI capabilities called “Machine Learning.”  (christophm.github)   How is ML different from AI? - Machine learning is not separate from AI; it is a subset of artificial intelligence. In general, Machine Learning is the smarter version of AI that does not require to program over and again. Basically, in AI, the software behaves according to the specific instructions (spoon-feeding) or program. But in ML, you don’t prompt the machine how to behave; all you have to do is feed the data and rest work is done by ML. Unlike AI, ML does not need millions of codes, complex rules, and decision trees to perform any particular task but instead uses algorithms that adjust itself and improves. It trains itself to change the algorithm as per the data input and takes action accordingly.   You can resemble it with manual vs. automatic cars. In a manual car, you have to shift the gears manually whenever you want to increase/decrease the speed of the car, in automatic cars the system takes care of the gear-box, and all you need to do is to control the acceleration padel.   Example of Machine Learning- One of the best examples of Machine learning is “product recommendation,” you too may have experienced this while accessing a shopping app or website when a message pops out recommending the product based on your past buying history. To see what is the difference between them, see the image below.   ( Image source: quora)  There are a few more details that set them apart from each other.  Machine learning vs. Artificial Intelligence  Machine learning has equipped the business owner to achieve pinpoint accuracy in reading customer’s demand and design products accordingly. The ML model can be used to segment customers, spot anomalies, or forecast sales. With AutoML kicking in, these processes will get even better, and companies that were reluctant to adopting ML application due to lack of expertise will now embrace it with open hands. AutoML will eliminate the labor-intensive job of choosing and tuning the machine-learning models.

Artificial intelligence is a simulation of the human brain in the best of its form. Their neural network is far quicker than human reflexes that could capture views even from a speeding train. Over the years, artificial intelligence has improved in its comprehensive skills and decision power by refining the accumulated data and analyzing them just as humans do. 

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It has been possible to wire electrical components to perform intellectual jobs. However, the data-scientist added one more tool to enhance AI capabilities called “Machine Learning.” 

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(christophm.github)  

  • How is ML different from AI? - Machine learning is not separate from AI; it is a subset of artificial intelligence. In general, Machine Learning is the smarter version of AI that does not require to program over and again. Basically, in AI, the software behaves according to the specific instructions (spoon-feeding) or program. But in ML, you don’t prompt the machine how to behave; all you have to do is feed the data and rest work is done by ML. Unlike AI, ML does not need millions of codes, complex rules, and decision trees to perform any particular task but instead uses algorithms that adjust itself and improves. It trains itself to change the algorithm as per the data input and takes action accordingly.  

You can resemble it with manual vs. automatic cars. In a manual car, you have to shift the gears manually whenever you want to increase/decrease the speed of the car, in automatic cars the system takes care of the gear-box, and all you need to do is to control the acceleration padel.  

  • Example of Machine Learning- One of the best examples of Machine learning is “product recommendation,” you too may have experienced this while accessing a shopping app or website when a message pops out recommending the product based on your past buying history. To see what is the difference between them, see the image below.  

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( Image source: quora) 

There are a few more details that set them apart from each other. 

Machine learning vs. Artificial Intelligence 

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Machine learning has equipped the business owner to achieve pinpoint accuracy in reading customer’s demand and design products accordingly. The ML model can be used to segment customers, spot anomalies, or forecast sales. With AutoML kicking in, these processes will get even better, and companies that were reluctant to adopting ML application due to lack of expertise will now embrace it with open hands. AutoML will eliminate the labor-intensive job of choosing and tuning the machine-learning models.

 The term brand is a very vast one and it includes various aspects and parameters; it is not as simple as it seems or sounds to be. Branding is more likely the aura, persona and identity of the business, service and product that a company has to offer.It is not concealed from any one that branding and marketing goes on simultaneously and majority of the times, it is referred as brand marketing. With that said, it also becomes clear that brand marketing refers to the process of creating long-term tactics that helps in doing promotional activities. Considering the market place as the area of play, brand marketing helps in portraying and reflecting the ideologies of the brands.Just like sentences cannot be written without having the knowledge about alphabets, words, spellings, Grammar; just like even the basic Mathematical problems cannot be solved without having the knowledge of calculation; even branding and marketing cannot be done by a novice if he or she does not have even the basic fundamental knowledge about the subject.When it comes to understanding the basics of brand marketing, these are the few fundamental things that one has to know:-  Understanding your own brand is importantBrand marketing differs from brand to brand. What can be done for a restaurant will differ from what should be done for a kitchen product! What is done for an eco-product will completely vary from what is done for a plastic product! One who is into the brand marketing must understand this and work according to the value proposition that your business has to offer.-  Understanding your target audience is importantYou must understand your potential customers. A luxury product will be more appealing to the upper strata and hence they should be positioned among-st the elite class of audience. Likewise an affordable product should target to reach the common folk.-  It is also important to look exclusive & stand outThe first thing a brand needs to flaunt is its difference from the rest. It has to highlight and focus on its USPs in order to look exclusive.-  Know the difference between potential consumers & customersCustomer satisfaction and experience are very important for every brand. While it goes for new customer hunt, it cannot afford losing the already existing ones. It is important to build rapport with your existing customers and find out ways to influence prospects getting converted into customers.-   Understand that communication is the keyCommunication mode or method cannot remain constant because the same set of communication will not please all. Language should not become a barrier when it comes to brand marketing and communication.-   Learn the importance of planning and strategyRight kind of plans paired with a sound quality strategy can do wonders and brand marketing needs to understand this besides caring to invest in both. Hope this will be helpful and guide you a little in understanding the basic norms of marketing and branding! May you foster a memorable market presence that drives great ROI for business!  
 The term brand is a very vast one and it includes various aspects and parameters; it is not as simple as it seems or sounds to be. Branding is more likely the aura, persona and identity of the business, service and product that a company has to offer.It is not concealed from any one that branding and marketing goes on simultaneously and majority of the times, it is referred as brand marketing. With that said, it also becomes clear that brand marketing refers to the process of creating long-term tactics that helps in doing promotional activities. Considering the market place as the area of play, brand marketing helps in portraying and reflecting the ideologies of the brands.Just like sentences cannot be written without having the knowledge about alphabets, words, spellings, Grammar; just like even the basic Mathematical problems cannot be solved without having the knowledge of calculation; even branding and marketing cannot be done by a novice if he or she does not have even the basic fundamental knowledge about the subject.When it comes to understanding the basics of brand marketing, these are the few fundamental things that one has to know:-  Understanding your own brand is importantBrand marketing differs from brand to brand. What can be done for a restaurant will differ from what should be done for a kitchen product! What is done for an eco-product will completely vary from what is done for a plastic product! One who is into the brand marketing must understand this and work according to the value proposition that your business has to offer.-  Understanding your target audience is importantYou must understand your potential customers. A luxury product will be more appealing to the upper strata and hence they should be positioned among-st the elite class of audience. Likewise an affordable product should target to reach the common folk.-  It is also important to look exclusive & stand outThe first thing a brand needs to flaunt is its difference from the rest. It has to highlight and focus on its USPs in order to look exclusive.-  Know the difference between potential consumers & customersCustomer satisfaction and experience are very important for every brand. While it goes for new customer hunt, it cannot afford losing the already existing ones. It is important to build rapport with your existing customers and find out ways to influence prospects getting converted into customers.-   Understand that communication is the keyCommunication mode or method cannot remain constant because the same set of communication will not please all. Language should not become a barrier when it comes to brand marketing and communication.-   Learn the importance of planning and strategyRight kind of plans paired with a sound quality strategy can do wonders and brand marketing needs to understand this besides caring to invest in both. Hope this will be helpful and guide you a little in understanding the basic norms of marketing and branding! May you foster a memorable market presence that drives great ROI for business!  

 

The term brand is a very vast one and it includes various aspects and parameters; it is not as simple as it seems or sounds to be. Branding is more likely the aura, persona and identity of the business, service and product that a company has to offer.

It is not concealed from any one that branding and marketing goes on simultaneously and majority of the times, it is referred as brand marketing. With that said, it also becomes clear that brand marketing refers to the process of creating long-term tactics that helps in doing promotional activities. Considering the market place as the area of play, brand marketing helps in portraying and reflecting the ideologies of the brands.

Just like sentences cannot be written without having the knowledge about alphabets, words, spellings, Grammar; just like even the basic Mathematical problems cannot be solved without having the knowledge of calculation; even branding and marketing cannot be done by a novice if he or she does not have even the basic fundamental knowledge about the subject.

When it comes to understanding the basics of brand marketing, these are the few fundamental things that one has to know:

-  Understanding your own brand is important

Brand marketing differs from brand to brand. What can be done for a restaurant will differ from what should be done for a kitchen product! What is done for an eco-product will completely vary from what is done for a plastic product! One who is into the brand marketing must understand this and work according to the value proposition that your business has to offer.

-  Understanding your target audience is important

You must understand your potential customers. A luxury product will be more appealing to the upper strata and hence they should be positioned among-st the elite class of audience. Likewise an affordable product should target to reach the common folk.

-  It is also important to look exclusive & stand out

The first thing a brand needs to flaunt is its difference from the rest. It has to highlight and focus on its USPs in order to look exclusive.

-  Know the difference between potential consumers & customers

Customer satisfaction and experience are very important for every brand. While it goes for new customer hunt, it cannot afford losing the already existing ones. It is important to build rapport with your existing customers and find out ways to influence prospects getting converted into customers.

-   Understand that communication is the key

Communication mode or method cannot remain constant because the same set of communication will not please all. Language should not become a barrier when it comes to brand marketing and communication.

-   Learn the importance of planning and strategy

Right kind of plans paired with a sound quality strategy can do wonders and brand marketing needs to understand this besides caring to invest in both.

 

Hope this will be helpful and guide you a little in understanding the basic norms of marketing and branding! May you foster a memorable market presence that drives great ROI for business!

 

 

Most of the time, when we cover how the digital revolution is taking over the multiple industries which are coming up, we are mostly talking about automation. However, there are several mind-blowing applications or AI tools available in the market that perform several complex tasks that humans can complete by consuming a lot of time, which they can complete in a snap. Soon to be launched, the ONPASSIVE platform is developing many AI-empowered tools that are cost-effective and highly productive, especially when any business wants tools to attract more traffic. In today’s data-driven world, just targeting text content to the audience is never enough to increase the conversion rate. Let’s cover ONPASSIVE tools that will help any business in their campaigns: AMP (Accelerated Mobile Pages): This is the technology that optimizes the website for mobile devices. It enables a company to create interactive content, and subscribers can connect or interact in multiple ways, like browsing through various galleries, shopping carts, or while modifying an order and via completing a survey. If any target audience does not support the AMP version not to worry, it will get replaced by a regular HTML version. Geo-targeting: Geo-targeting is sending promotional content via text or notification based on customers’ real-time geo-location. The tool of geographical location which is currently employed by most of the businesses is geo-targeting, but geo-fencing is among the tools which have gained a name in very little time. Geo-targeting triggers the notifications based on the location (Country, region, or city), whereas geo-fencing triggers the announcements based on their site that is supposed to be within the radius of 1-2 km. Exceptional segmentation: This ONPASSIVE tool has capabilities to segment the data and analyze customer behavior to find out patterns enabling a company to target the potential customers at the right time to enhance the conversion rate of a business. High-level audience segmentation empowers a business to offer your possible audience with relevant products based on their activities. Personalized Notification: Content personalization represents the direct targeting of specific audiences with specific products, and you can create content according to potential customers. By segmenting the target audience, your business can approach different audiences based on their particular characteristic or the intention of your business. Releasing the time of push notification could be according to customer interaction. Push notifications may incorporate reminders of products in favorite lists or cart list. Gamification: Gamification is one of the best ways to interact with new subscribers or to activate the sleeping subscribers. This interactive element is not for direct sales. Still, in the end, it leads audiences towards a certain amount of discount or offers for their purchase, which increases the sale opportunities for the business. The only thing a business needs to keep in mind is the policies of participation and claiming for reward should be as transparent as water. It is the time for the business to employ AI in its process with the ONPASSIVE platform, which serves them to grow along with their business. The new emerging companies should leverage AI-powered tools offered by ONPASSIVE for the smooth running of their business process. ONPASSIVE is a soon to be launched business solution platform which enables any business to grow by leveraging AI capabilities in their tools to meet modern-day advance audiences’ expectation.
Most of the time, when we cover how the digital revolution is taking over the multiple industries which are coming up, we are mostly talking about automation. However, there are several mind-blowing applications or AI tools available in the market that perform several complex tasks that humans can complete by consuming a lot of time, which they can complete in a snap. Soon to be launched, the ONPASSIVE platform is developing many AI-empowered tools that are cost-effective and highly productive, especially when any business wants tools to attract more traffic. In today’s data-driven world, just targeting text content to the audience is never enough to increase the conversion rate. Let’s cover ONPASSIVE tools that will help any business in their campaigns: AMP (Accelerated Mobile Pages): This is the technology that optimizes the website for mobile devices. It enables a company to create interactive content, and subscribers can connect or interact in multiple ways, like browsing through various galleries, shopping carts, or while modifying an order and via completing a survey. If any target audience does not support the AMP version not to worry, it will get replaced by a regular HTML version. Geo-targeting: Geo-targeting is sending promotional content via text or notification based on customers’ real-time geo-location. The tool of geographical location which is currently employed by most of the businesses is geo-targeting, but geo-fencing is among the tools which have gained a name in very little time. Geo-targeting triggers the notifications based on the location (Country, region, or city), whereas geo-fencing triggers the announcements based on their site that is supposed to be within the radius of 1-2 km. Exceptional segmentation: This ONPASSIVE tool has capabilities to segment the data and analyze customer behavior to find out patterns enabling a company to target the potential customers at the right time to enhance the conversion rate of a business. High-level audience segmentation empowers a business to offer your possible audience with relevant products based on their activities. Personalized Notification: Content personalization represents the direct targeting of specific audiences with specific products, and you can create content according to potential customers. By segmenting the target audience, your business can approach different audiences based on their particular characteristic or the intention of your business. Releasing the time of push notification could be according to customer interaction. Push notifications may incorporate reminders of products in favorite lists or cart list. Gamification: Gamification is one of the best ways to interact with new subscribers or to activate the sleeping subscribers. This interactive element is not for direct sales. Still, in the end, it leads audiences towards a certain amount of discount or offers for their purchase, which increases the sale opportunities for the business. The only thing a business needs to keep in mind is the policies of participation and claiming for reward should be as transparent as water. It is the time for the business to employ AI in its process with the ONPASSIVE platform, which serves them to grow along with their business. The new emerging companies should leverage AI-powered tools offered by ONPASSIVE for the smooth running of their business process. ONPASSIVE is a soon to be launched business solution platform which enables any business to grow by leveraging AI capabilities in their tools to meet modern-day advance audiences’ expectation.

Most of the time, when we cover how the digital revolution is taking over the multiple industries which are coming up, we are mostly talking about automation. However, there are several mind-blowing applications or AI tools available in the market that perform several complex tasks that humans can complete by consuming a lot of time, which they can complete in a snap.

Soon to be launched, the ONPASSIVE platform is developing many AI-empowered tools that are cost-effective and highly productive, especially when any business wants tools to attract more traffic.

In today’s data-driven world, just targeting text content to the audience is never enough to increase the conversion rate. Let’s cover ONPASSIVE tools that will help any business in their campaigns:

AMP (Accelerated Mobile Pages):

This is the technology that optimizes the website for mobile devices. It enables a company to create interactive content, and subscribers can connect or interact in multiple ways, like browsing through various galleries, shopping carts, or while modifying an order and via completing a survey. If any target audience does not support the AMP version not to worry, it will get replaced by a regular HTML version.

Geo-targeting:

Geo-targeting is sending promotional content via text or notification based on customers’ real-time geo-location. The tool of geographical location which is currently employed by most of the businesses is geo-targeting, but geo-fencing is among the tools which have gained a name in very little time. Geo-targeting triggers the notifications based on the location (Country, region, or city), whereas geo-fencing triggers the announcements based on their site that is supposed to be within the radius of 1-2 km.

Exceptional segmentation:

This ONPASSIVE tool has capabilities to segment the data and analyze customer behavior to find out patterns enabling a company to target the potential customers at the right time to enhance the conversion rate of a business. High-level audience segmentation empowers a business to offer your possible audience with relevant products based on their activities.

Personalized Notification:

Content personalization represents the direct targeting of specific audiences with specific products, and you can create content according to potential customers. By segmenting the target audience, your business can approach different audiences based on their particular characteristic or the intention of your business. Releasing the time of push notification could be according to customer interaction. Push notifications may incorporate reminders of products in favorite lists or cart list.

Gamification:

Gamification is one of the best ways to interact with new subscribers or to activate the sleeping subscribers. This interactive element is not for direct sales. Still, in the end, it leads audiences towards a certain amount of discount or offers for their purchase, which increases the sale opportunities for the business. The only thing a business needs to keep in mind is the policies of participation and claiming for reward should be as transparent as water.

It is the time for the business to employ AI in its process with the ONPASSIVE platform, which serves them to grow along with their business. The new emerging companies should leverage AI-powered tools offered by ONPASSIVE for the smooth running of their business process.

ONPASSIVE is a soon to be launched business solution platform which enables any business to grow by leveraging AI capabilities in their tools to meet modern-day advance audiences’ expectation.

 Corporate learning management systems (LMS) software system provides essentials for the training and learning of employees and customers. Often, the courses for customers are designed with the purpose of delivering training resources along with the product or as a standalone product. Courses for employees are designed with the motive of skill development.Commonly, most Corporate LMS include tools for content authoring, content library, reporting and analytics. However, a modernize LMS must provide the following:Content creation and customizationCourse managementContent library managementInteractive video-based coachingPlatform compatibilityTools to perform assessmentsCertificationTrack performanceGenerate reportsIntegration of widgets, E-commerce and Social mediaLive sessionsVR learningBased on enterprise requirements, one can choose from independent software or HR suite. In the present competitive world, companies are inclined towards training their workforce with skills to help them improve efficiency and excel in their career path.1. Docebo: This AI-powered LMS software can automate learning such that social, formal and informal learning experiences are achieved on a single platform. Docebo automatically analyzes the content of a course and recommend admin.Features:The application contains over 20,000 on-demand coursesPermits uploading different file formatsIntegrating with other application for content creationDesigning a fixed learning pathGaming featuresEasy enrollment and deactivationCertificationDashboard to track activitiesGenerate custom reportsConfigure alerts and notificationsMobile compatibility for Android and iOS devices.It is based on a 70/20/10 model that stands for 70% assignment work, 20% developmental relationship and 10% training and course creation.  With over 1500 clients in 80 countries, Docebo is purpose-driven to help companies scale their training with ease.2. Adobe Captivate Prime:Adobe offers an award-winning LMS to create learning programs and monitor their impact on the growth of the business. The striking component of Captivate is its stimulating learning experience that keeps the learners engrossed. Businesses can earn maximum from their training programs.Features:Blended learningLearner’s portalSCORM complianceSynchronous and asynchronous learningGamificationMobile LearningWebinarsAdobe Captivate Prime is recognized by top analysts & adopted by leading corporations across the globe.3. SAP Litmos:With their learner-centric approach, SAP Litmos offers learning solutions for employees, customers and partners. The platform serves the industry needs addressing areas of security, scalability and user-friendliness. Features:Multiple media format supportTest and quizzesCourse libraryReport creationTracking performanceSCORM & TIN CAN complianceE-commerce integrationGamificationNotification and alertsCertificationsSummary: It is recommended to take time out for some research so that you can compare and find the LMS that best suits your learning needs and budget.
 Corporate learning management systems (LMS) software system provides essentials for the training and learning of employees and customers. Often, the courses for customers are designed with the purpose of delivering training resources along with the product or as a standalone product. Courses for employees are designed with the motive of skill development.Commonly, most Corporate LMS include tools for content authoring, content library, reporting and analytics. However, a modernize LMS must provide the following:Content creation and customizationCourse managementContent library managementInteractive video-based coachingPlatform compatibilityTools to perform assessmentsCertificationTrack performanceGenerate reportsIntegration of widgets, E-commerce and Social mediaLive sessionsVR learningBased on enterprise requirements, one can choose from independent software or HR suite. In the present competitive world, companies are inclined towards training their workforce with skills to help them improve efficiency and excel in their career path.1. Docebo: This AI-powered LMS software can automate learning such that social, formal and informal learning experiences are achieved on a single platform. Docebo automatically analyzes the content of a course and recommend admin.Features:The application contains over 20,000 on-demand coursesPermits uploading different file formatsIntegrating with other application for content creationDesigning a fixed learning pathGaming featuresEasy enrollment and deactivationCertificationDashboard to track activitiesGenerate custom reportsConfigure alerts and notificationsMobile compatibility for Android and iOS devices.It is based on a 70/20/10 model that stands for 70% assignment work, 20% developmental relationship and 10% training and course creation.  With over 1500 clients in 80 countries, Docebo is purpose-driven to help companies scale their training with ease.2. Adobe Captivate Prime:Adobe offers an award-winning LMS to create learning programs and monitor their impact on the growth of the business. The striking component of Captivate is its stimulating learning experience that keeps the learners engrossed. Businesses can earn maximum from their training programs.Features:Blended learningLearner’s portalSCORM complianceSynchronous and asynchronous learningGamificationMobile LearningWebinarsAdobe Captivate Prime is recognized by top analysts & adopted by leading corporations across the globe.3. SAP Litmos:With their learner-centric approach, SAP Litmos offers learning solutions for employees, customers and partners. The platform serves the industry needs addressing areas of security, scalability and user-friendliness. Features:Multiple media format supportTest and quizzesCourse libraryReport creationTracking performanceSCORM & TIN CAN complianceE-commerce integrationGamificationNotification and alertsCertificationsSummary: It is recommended to take time out for some research so that you can compare and find the LMS that best suits your learning needs and budget.

 

Corporate learning management systems (LMS) software system provides essentials for the training and learning of employees and customers. Often, the courses for customers are designed with the purpose of delivering training resources along with the product or as a standalone product. Courses for employees are designed with the motive of skill development.

Commonly, most Corporate LMS include tools for content authoring, content library, reporting and analytics. However, a modernize LMS must provide the following:

  • Content creation and customization
  • Course management
  • Content library management
  • Interactive video-based coaching
  • Platform compatibility
  • Tools to perform assessments
  • Certification
  • Track performance
  • Generate reports
  • Integration of widgets, E-commerce and Social media
  • Live sessions
  • VR learning

Based on enterprise requirements, one can choose from independent software or HR suite. In the present competitive world, companies are inclined towards training their workforce with skills to help them improve efficiency and excel in their career path.

1. Docebo: 

This AI-powered LMS software can automate learning such that social, formal and informal learning experiences are achieved on a single platform. Docebo automatically analyzes the content of a course and recommend admin.

Features:

  • The application contains over 20,000 on-demand courses
  • Permits uploading different file formats
  • Integrating with other application for content creation
  • Designing a fixed learning path
  • Gaming features
  • Easy enrollment and deactivation
  • Certification
  • Dashboard to track activities
  • Generate custom reports
  • Configure alerts and notifications
  • Mobile compatibility for Android and iOS devices.

It is based on a 70/20/10 model that stands for 70% assignment work, 20% developmental relationship and 10% training and course creation.  

With over 1500 clients in 80 countries, Docebo is purpose-driven to help companies scale their training with ease.

2. Adobe Captivate Prime:

Adobe offers an award-winning LMS to create learning programs and monitor their impact on the growth of the business. The striking component of Captivate is its stimulating learning experience that keeps the learners engrossed. Businesses can earn maximum from their training programs.

Features:

  • Blended learning
  • Learner’s portal
  • SCORM compliance
  • Synchronous and asynchronous learning
  • Gamification
  • Mobile Learning
  • Webinars

Adobe Captivate Prime is recognized by top analysts & adopted by leading corporations across the globe.

3. SAP Litmos:

With their learner-centric approach, SAP Litmos offers learning solutions for employees, customers and partners. The platform serves the industry needs addressing areas of security, scalability and user-friendliness. 

Features:

  • Multiple media format support
  • Test and quizzes
  • Course library
  • Report creation
  • Tracking performance
  • SCORM & TIN CAN compliance
  • E-commerce integration
  • Gamification
  • Notification and alerts
  • Certifications

Summary:

 It is recommended to take time out for some research so that you can compare and find the LMS that best suits your learning needs and budget.

Data science and Python are a perfect union of modern science. You may call it a coincidence or technology revolution phase, the fact is: they resonate with each other perfectly.  Their camaraderie helped data-scientists to develop some best scientific applications that involved complex calculations. The object-oriented approach of Python language gels well with Data Science.Data science spans three designations for the professionals interested in this field, 1) Data Analysts 2) Data Scientists3) Data engineersThese professionals are highly talented and capable of building complex quantitative algorithms. They organize and synthesize large amounts of data used to answer questions and drive strategy in their organization.Steps to learn data science with PythonStep 1) Introduction to data scienceGet a general overview of Data Science. Then learn how Python is deployed for data science applications and various steps involved in the Data Science process like data wrangling, data exploration, and selecting the model.Step 2) Having a good hold over Python language and their libraries  Complete knowledge of Python programming language is essential for data-science, particularly the scientific libraries.  Learn Scientific libraries in Python – SciPy, NumPy, Matplotlib and PandasPractice the NumPy thoroughly, especially NumPy arrays.Go through the basics and practice SciPyThe next stage is to get hands-on Matplotlib. It is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib can be used in Python, Python scripts, and IPython shell, web application servers, and various graphical user interface toolkits.Finally, brush your knowledge on Pandas. It provides DataFrame functionality (like R) for Python. It is recommended that you spend a good time practicing Pandas. It would become the most effective tool for all mid-size data analysis.Also, learn machine learning and natural language processing with Sci-kit.It is an advantage if you have a clear concept of K-Means Clustering, Logistic Regression, and Linear Regression. It is very valuable with respect to preparing a machine learning algorithmThe individual should also hone their skills in web scraping with BeautifulSoup. Python integration with Hadoop MapReduce and Spark.Step 3) Practise Mini-ProjectsThe data-science enthusiasts on initial bases can improve their knowledge by working with Mini-Projects.  While working with a mini-project, try to learn advanced data science techniques.  You can try machine learning – bootstrapping models and creating neural networks using scikit-learn. . There are many online sources free as well as paid that could assist you in learning data science with Python.   Here is the list of free courses to learn Data Science with Python 1) Computer Science & Programming Using PythonOffered by: MITx on edXDuration: 9 weeksSkill level: IntroductoryTechnology requirements: Basic algebra and some background knowledge of programming2) Statistics With Python SpecializationOffered by: University of Michigan on CourseraDuration: 8 weeksSkill level: IntroductoryTechnology requirements: Basic linear algebra & calculus3) Data Science: Machine LearningOffered by: Harvard on edXDuration: 8 weeksSkill level: IntroductoryTechnology requirements: An up-to-date browser to enable programming directly in a browser-based interface.4) Data Science EthicsOffered by: University of Michigan on CourseraDuration: 4 weeksSkill level: Introductory5) Introduction to Python and Data-scienceOffered by: Analytics VidhyaDuration: Depends on courseSkill level: Intermediate6) Data Scientist in PythonOffered by: DataquestDuration: Depends on courseSkill level: Intermediate to high level  Paid courses to learn Data-ScienceUdemy- Python for Data Science and Machine Learning BootcampIntellipaat- Python for Data ScienceUdacity- Programming for Data Science with PythonData-Science Pro-skillsFrom an absolute beginner to a pro in the journey of learning data science, you might be using all sets of skills or technology mentioned below. So, it is preferable to tap on these technology stacks as well.(Image source: datascience.berkeley. edu)
Data science and Python are a perfect union of modern science. You may call it a coincidence or technology revolution phase, the fact is: they resonate with each other perfectly.  Their camaraderie helped data-scientists to develop some best scientific applications that involved complex calculations. The object-oriented approach of Python language gels well with Data Science.Data science spans three designations for the professionals interested in this field, 1) Data Analysts 2) Data Scientists3) Data engineersThese professionals are highly talented and capable of building complex quantitative algorithms. They organize and synthesize large amounts of data used to answer questions and drive strategy in their organization.Steps to learn data science with PythonStep 1) Introduction to data scienceGet a general overview of Data Science. Then learn how Python is deployed for data science applications and various steps involved in the Data Science process like data wrangling, data exploration, and selecting the model.Step 2) Having a good hold over Python language and their libraries  Complete knowledge of Python programming language is essential for data-science, particularly the scientific libraries.  Learn Scientific libraries in Python – SciPy, NumPy, Matplotlib and PandasPractice the NumPy thoroughly, especially NumPy arrays.Go through the basics and practice SciPyThe next stage is to get hands-on Matplotlib. It is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib can be used in Python, Python scripts, and IPython shell, web application servers, and various graphical user interface toolkits.Finally, brush your knowledge on Pandas. It provides DataFrame functionality (like R) for Python. It is recommended that you spend a good time practicing Pandas. It would become the most effective tool for all mid-size data analysis.Also, learn machine learning and natural language processing with Sci-kit.It is an advantage if you have a clear concept of K-Means Clustering, Logistic Regression, and Linear Regression. It is very valuable with respect to preparing a machine learning algorithmThe individual should also hone their skills in web scraping with BeautifulSoup. Python integration with Hadoop MapReduce and Spark.Step 3) Practise Mini-ProjectsThe data-science enthusiasts on initial bases can improve their knowledge by working with Mini-Projects.  While working with a mini-project, try to learn advanced data science techniques.  You can try machine learning – bootstrapping models and creating neural networks using scikit-learn. . There are many online sources free as well as paid that could assist you in learning data science with Python.   Here is the list of free courses to learn Data Science with Python 1) Computer Science & Programming Using PythonOffered by: MITx on edXDuration: 9 weeksSkill level: IntroductoryTechnology requirements: Basic algebra and some background knowledge of programming2) Statistics With Python SpecializationOffered by: University of Michigan on CourseraDuration: 8 weeksSkill level: IntroductoryTechnology requirements: Basic linear algebra & calculus3) Data Science: Machine LearningOffered by: Harvard on edXDuration: 8 weeksSkill level: IntroductoryTechnology requirements: An up-to-date browser to enable programming directly in a browser-based interface.4) Data Science EthicsOffered by: University of Michigan on CourseraDuration: 4 weeksSkill level: Introductory5) Introduction to Python and Data-scienceOffered by: Analytics VidhyaDuration: Depends on courseSkill level: Intermediate6) Data Scientist in PythonOffered by: DataquestDuration: Depends on courseSkill level: Intermediate to high level  Paid courses to learn Data-ScienceUdemy- Python for Data Science and Machine Learning BootcampIntellipaat- Python for Data ScienceUdacity- Programming for Data Science with PythonData-Science Pro-skillsFrom an absolute beginner to a pro in the journey of learning data science, you might be using all sets of skills or technology mentioned below. So, it is preferable to tap on these technology stacks as well.(Image source: datascience.berkeley. edu)

Data science and Python are a perfect union of modern science. You may call it a coincidence or technology revolution phase, the fact is: they resonate with each other perfectly.  Their camaraderie helped data-scientists to develop some best scientific applications that involved complex calculations. The object-oriented approach of Python language gels well with Data Science.

Data science spans three designations for the professionals interested in this field, 

1) Data Analysts 

2) Data Scientists

3) Data engineers

These professionals are highly talented and capable of building complex quantitative algorithms. They organize and synthesize large amounts of data used to answer questions and drive strategy in their organization.

Steps to learn data science with Python

Step 1) Introduction to data science

Get a general overview of Data Science. Then learn how Python is deployed for data science applications and various steps involved in the Data Science process like data wrangling, data exploration, and selecting the model.

Step 2) Having a good hold over Python language and their libraries

  

Complete knowledge of Python programming language is essential for data-science, particularly the scientific libraries.  

Learn Scientific libraries in Python – SciPy, NumPy, Matplotlib and Pandas

  • Practice the NumPy thoroughly, especially NumPy arrays.
  • Go through the basics and practice SciPy
  • The next stage is to get hands-on Matplotlib. It is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib can be used in Python, Python scripts, and IPython shell, web application servers, and various graphical user interface toolkits.
  • Finally, brush your knowledge on Pandas. It provides DataFrame functionality (like R) for Python. It is recommended that you spend a good time practicing Pandas. It would become the most effective tool for all mid-size data analysis.
  • Also, learn machine learning and natural language processing with Sci-kit.
  • It is an advantage if you have a clear concept of K-Means Clustering, Logistic Regression, and Linear Regression. It is very valuable with respect to preparing a machine learning algorithm
  • The individual should also hone their skills in web scraping with BeautifulSoup. Python integration with Hadoop MapReduce and Spark.

Step 3) Practise Mini-Projects

The data-science enthusiasts on initial bases can improve their knowledge by working with Mini-Projects.  While working with a mini-project, try to learn advanced data science techniques.  You can try machine learning – bootstrapping models and creating neural networks using scikit-learn. . 

There are many online sources free as well as paid that could assist you in learning data science with Python.  

 

Here is the list of free courses to learn Data Science with Python

 

1) Computer Science & Programming Using Python

Offered by: MITx on edX

Duration: 9 weeks

Skill level: Introductory

Technology requirements: Basic algebra and some background knowledge of programming

2) Statistics With Python Specialization

Offered by: University of Michigan on Coursera

Duration: 8 weeks

Skill level: Introductory

Technology requirements: Basic linear algebra & calculus

3) Data Science: Machine Learning

Offered by: Harvard on edX

Duration: 8 weeks

Skill level: Introductory

Technology requirements: An up-to-date browser to enable programming directly in a browser-based interface.

4) Data Science Ethics

Offered by: University of Michigan on Coursera

Duration: 4 weeks

Skill level: Introductory

5) Introduction to Python and Data-science

Offered by: Analytics Vidhya

Duration: Depends on course

Skill level: Intermediate

6) Data Scientist in Python

Offered by: Dataquest

Duration: Depends on course

Skill level: Intermediate to high level 

 

Paid courses to learn Data-Science

  1. Udemy- Python for Data Science and Machine Learning Bootcamp
  2. Intellipaat- Python for Data Science
  3. Udacity- Programming for Data Science with Python

Data-Science Pro-skills

From an absolute beginner to a pro in the journey of learning data science, you might be using all sets of skills or technology mentioned below. So, it is preferable to tap on these technology stacks as well.

(Image source: datascience.berkeley. edu)

While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:If accuracy is a concern, test different algorithms and cross-validate themIf the training dataset is small, use models that have low variance and high biasIf the training dataset is large, use models that have high variance and little bias
While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:If accuracy is a concern, test different algorithms and cross-validate themIf the training dataset is small, use models that have low variance and high biasIf the training dataset is large, use models that have high variance and little bias

While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:

  • If accuracy is a concern, test different algorithms and cross-validate them
  • If the training dataset is small, use models that have low variance and high bias
  • If the training dataset is large, use models that have high variance and little bias
Learning Management System is essential for the success of any organization today. It is an integral part of the corporate strategy. Customer training is an informal form of customer service that gives clients the resources and knowledge to improve interaction with your business.A Learning Management System in Customer Training helps to understand the customer demand and hence the requirements of the organization. The software allows companies to achieve higher customer retention and engagement. It facilitates enterprises in delivering the required training, awareness or guidance for their product and further analyzes the behavior of the customer.Apart from all this, it also provides managers with various other tools like web-based training modules which can be easily used in various training. It helps in saving on valuable time and money that could be otherwise spent in hiring and deploying staff.There is a wide variety of LMSs out there in the market. I have mentioned here the best three:1. Docebo: The platform is designed to deliver memorable learning experiences. Let us look at its key features:Docebo is compatible with platforms such as Windows, Linux and Mac.Allows integration with Adobe connect, G Suite, CRM, Shopify, Slack, WebEx, WooCommerce, WordPress, Zoom,  Zapier and many more. Docebo training can be done in the form of webinars, documentation & online tutorials.Deployment on Desktop, Mobile and SaaS/Cloud.Centralize, manage & automate learning with Docebo.2. Adobe Captivate Prime: The system implies a learner-first approach rendering personalization, high-quality video streaming, mobile-friendliness and many other functionalities to conduct customer training with ease. Features include:Platform compatibility for Mac and Windows.Browser compatibility for Apple Safari, Google Chrome and Internet Explorer.The application allows integration with  Adobe Connect, API, Okta, Outlook, Calendar, Salesforce CRM, Shopify, WebEX, WordPress, Zendesk, Mailchimp and Zoom.Training can be conducted through documentation, online tutorials and webinars.Peer-peer learning through automated content curation.3. SAP Litmos: LMS empowers businesses to create, collaborate, distribute globally, and schedule as well as track their web-based customer training courses. Features extended by this application are:Platform compatibility for Mac, Linux and Windows.Browser compatibility for Apple Safari, Google Chrome, Internet Explorer and Mozilla Firefox.Deployment on Mobile and SaaS/Cloud.The application allows integration with Drupal, Google Calendar, Salesforce CRM, Slack and Zoom.Training can be conducted through documentation, online tutorials and webinars.Deploys in minutes and scales to millions of users.In final words, communicating with the right and valuable content is the key to gaining higher customer engagement. It can have a great impact on your business. The right LMS will help you communicate with your prospects and benefit from customer retention.
Learning Management System is essential for the success of any organization today. It is an integral part of the corporate strategy. Customer training is an informal form of customer service that gives clients the resources and knowledge to improve interaction with your business.A Learning Management System in Customer Training helps to understand the customer demand and hence the requirements of the organization. The software allows companies to achieve higher customer retention and engagement. It facilitates enterprises in delivering the required training, awareness or guidance for their product and further analyzes the behavior of the customer.Apart from all this, it also provides managers with various other tools like web-based training modules which can be easily used in various training. It helps in saving on valuable time and money that could be otherwise spent in hiring and deploying staff.There is a wide variety of LMSs out there in the market. I have mentioned here the best three:1. Docebo: The platform is designed to deliver memorable learning experiences. Let us look at its key features:Docebo is compatible with platforms such as Windows, Linux and Mac.Allows integration with Adobe connect, G Suite, CRM, Shopify, Slack, WebEx, WooCommerce, WordPress, Zoom,  Zapier and many more. Docebo training can be done in the form of webinars, documentation & online tutorials.Deployment on Desktop, Mobile and SaaS/Cloud.Centralize, manage & automate learning with Docebo.2. Adobe Captivate Prime: The system implies a learner-first approach rendering personalization, high-quality video streaming, mobile-friendliness and many other functionalities to conduct customer training with ease. Features include:Platform compatibility for Mac and Windows.Browser compatibility for Apple Safari, Google Chrome and Internet Explorer.The application allows integration with  Adobe Connect, API, Okta, Outlook, Calendar, Salesforce CRM, Shopify, WebEX, WordPress, Zendesk, Mailchimp and Zoom.Training can be conducted through documentation, online tutorials and webinars.Peer-peer learning through automated content curation.3. SAP Litmos: LMS empowers businesses to create, collaborate, distribute globally, and schedule as well as track their web-based customer training courses. Features extended by this application are:Platform compatibility for Mac, Linux and Windows.Browser compatibility for Apple Safari, Google Chrome, Internet Explorer and Mozilla Firefox.Deployment on Mobile and SaaS/Cloud.The application allows integration with Drupal, Google Calendar, Salesforce CRM, Slack and Zoom.Training can be conducted through documentation, online tutorials and webinars.Deploys in minutes and scales to millions of users.In final words, communicating with the right and valuable content is the key to gaining higher customer engagement. It can have a great impact on your business. The right LMS will help you communicate with your prospects and benefit from customer retention.

Learning Management System is essential for the success of any organization today. It is an integral part of the corporate strategy. Customer training is an informal form of customer service that gives clients the resources and knowledge to improve interaction with your business.

A Learning Management System in Customer Training helps to understand the customer demand and hence the requirements of the organization. The software allows companies to achieve higher customer retention and engagement. 

It facilitates enterprises in delivering the required training, awareness or guidance for their product and further analyzes the behavior of the customer.

Apart from all this, it also provides managers with various other tools like web-based training modules which can be easily used in various training. It helps in saving on valuable time and money that could be otherwise spent in hiring and deploying staff.

There is a wide variety of LMSs out there in the market. I have mentioned here the best three:

1. Docebo: The platform is designed to deliver memorable learning experiences. Let us look at its key features:

  • Docebo is compatible with platforms such as Windows, Linux and Mac.
  • Allows integration with Adobe connect, G Suite, CRM, Shopify, Slack, WebEx, WooCommerce, WordPress, Zoom,  Zapier and many more. 
  • Docebo training can be done in the form of webinars, documentation & online tutorials.
  • Deployment on Desktop, Mobile and SaaS/Cloud.
  • Centralize, manage & automate learning with Docebo.

2. Adobe Captivate Prime: The system implies a learner-first approach rendering personalization, high-quality video streaming, mobile-friendliness and many other functionalities to conduct customer training with ease. Features include:

  • Platform compatibility for Mac and Windows.
  • Browser compatibility for Apple Safari, Google Chrome and Internet Explorer.
  • The application allows integration with  Adobe Connect, API, Okta, Outlook, Calendar, Salesforce CRM, Shopify, WebEX, WordPress, Zendesk, Mailchimp and Zoom.
  • Training can be conducted through documentation, online tutorials and webinars.
  • Peer-peer learning through automated content curation.

3. SAP Litmos: LMS empowers businesses to create, collaborate, distribute globally, and schedule as well as track their web-based customer training courses. Features extended by this application are:

  • Platform compatibility for Mac, Linux and Windows.
  • Browser compatibility for Apple Safari, Google Chrome, Internet Explorer and Mozilla Firefox.
  • Deployment on Mobile and SaaS/Cloud.
  • The application allows integration with Drupal, Google Calendar, Salesforce CRM, Slack and Zoom.
  • Training can be conducted through documentation, online tutorials and webinars.
  • Deploys in minutes and scales to millions of users.

In final words, communicating with the right and valuable content is the key to gaining higher customer engagement. It can have a great impact on your business. The right LMS will help you communicate with your prospects and benefit from customer retention.

Machine Learning is the top domain in computer science these days. It is one of the evolving technologies with many organizations in different sectors. This technology can be beneficial for professionals working in various sectors. So, if you’re a professional seeking online courses for machine learning, many platforms provide the material.You can get to know the basics of Machine Learning from these online portals and then implement these concepts in projects. These courses can help you get a new job or switch careers, or want to learn something new.Please find some of the best machine learning course provider portals below:1. Machine Learning by Stanford UniversityThis is one of the most well-known Machine Learning courses available on the web. This course is taught by Andrew Ng, the former Chief Scientist for Baidu and Director of the Google Brain Deep Learning Project. You can learn both the theoretical aspects of Machine Learning algorithms and the practical implementations of it with the help of this course. Moreover, this course provides a full set of Linear Regression with One & Multiple Variable, Logistic Regression, Regularization, Neural Networks, Support Vector Machines, Unsupervised Learning, and many more implementing Octave or MATLAB. It takes about 11 weeks to complete this course, and it covers various aspects and applications of Machine Learning. These learning algorithms can help computer vision, database mining, text-understanding, designing robots, and several other applications. Upon completing this course, you receive a Shareable Certificate that you can attach to your resume or LinkedIn profile.2. Deep Learning Specialization by DeepLearningAIDeep Learning Specialization is an advanced specialization course by Andrew NG. You can go for this course after successful completion of the basic Machine Learning course. This course covers various aspects such as Convolutional Networks, Recurrent neural networks, Long short-term memory (LSTM), Natural Language Processing, and numerous others. Moreover, this course also provides personal stories and career advice from many top leaders in Deep Learning, which will enhance your experience. Deep Learning specialization offers five times comprising Neural Networks & Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models. This stream helps you create deep learning models in different fields such as autonomous driving, healthcare, natural language processing, music generation, and many more. Once you complete each of these courses successfully in specialization, you will receive a Shareable Certificate.3. Machine Learning with Python by IBMMachine Learning with Python teaches the applications of Machine Learning and its applications in the real world using Python. After learning the basics of machine learning, you can move on to its algorithms, such as Regression, Classification, and Clustering algorithms. The course is further classified into six weeks, with each of them targetting on an Introduction to Machine Learning, Regression algorithms including Linear, Non-linear, Simple, and Multiple regression, Classification algorithms including  SVM, Decision Trees, KNN, Logistic Regression, and more. Moreover, the clustering algorithms comprises of Hierarchical Clustering, Partitioned-based Clustering, and Density-based Clustering, Recommender Systems, and the final week inclusive of the Final Project that would implement everything you have learned.4. Machine Learning by HarvardXThis course provides knowledge about the basics of Machine Learning and its various learning algorithms,v principal component analysis, and regularization by developing a movie recommender system. It also provides knowledge about data analysis and training data to receive useful insights. This course majorly focuses on ML algorithms like Linear Regression with One Variable, Linear Regression with Multiple Variables, Logistic Regression, Support Vector Machines, Unsupervised Learning, and a few others. Along with all the ML algorithms, it also teaches you cross-validation to avoid overtraining that data. You can prevail in an instructor-signed certificate from edX, and HarvardX is demonstrating your knowledge of Machine Learning for Data Science and analytics on successful completion of the course.5. Principles of Machine Learning by MicrosoftThis course covers basic principles of Machine Learning that are taught using theory and then followed by practical experiences. These experiences are made by creating and deploying ML models. You can use Python and Azure Notebooks to design these models and procure data insights. Furthermore, this course offers an introduction to Machine Learning and introduces you with data exploration, data preparation, Machine Learning algorithms. These algorithms comprise Supervised & Unsupervised Learning and improving model performance. Same as above, you would receive an instructor-attested certificate from edX and Microsoft to demonstrate your knowledge of Machine Learning for Data Science and Analytics at the end of the course.All the courses mentioned above offer the best machine learning courses from basic to advanced levels.
Machine Learning is the top domain in computer science these days. It is one of the evolving technologies with many organizations in different sectors. This technology can be beneficial for professionals working in various sectors. So, if you’re a professional seeking online courses for machine learning, many platforms provide the material.You can get to know the basics of Machine Learning from these online portals and then implement these concepts in projects. These courses can help you get a new job or switch careers, or want to learn something new.Please find some of the best machine learning course provider portals below:1. Machine Learning by Stanford UniversityThis is one of the most well-known Machine Learning courses available on the web. This course is taught by Andrew Ng, the former Chief Scientist for Baidu and Director of the Google Brain Deep Learning Project. You can learn both the theoretical aspects of Machine Learning algorithms and the practical implementations of it with the help of this course. Moreover, this course provides a full set of Linear Regression with One & Multiple Variable, Logistic Regression, Regularization, Neural Networks, Support Vector Machines, Unsupervised Learning, and many more implementing Octave or MATLAB. It takes about 11 weeks to complete this course, and it covers various aspects and applications of Machine Learning. These learning algorithms can help computer vision, database mining, text-understanding, designing robots, and several other applications. Upon completing this course, you receive a Shareable Certificate that you can attach to your resume or LinkedIn profile.2. Deep Learning Specialization by DeepLearningAIDeep Learning Specialization is an advanced specialization course by Andrew NG. You can go for this course after successful completion of the basic Machine Learning course. This course covers various aspects such as Convolutional Networks, Recurrent neural networks, Long short-term memory (LSTM), Natural Language Processing, and numerous others. Moreover, this course also provides personal stories and career advice from many top leaders in Deep Learning, which will enhance your experience. Deep Learning specialization offers five times comprising Neural Networks & Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models. This stream helps you create deep learning models in different fields such as autonomous driving, healthcare, natural language processing, music generation, and many more. Once you complete each of these courses successfully in specialization, you will receive a Shareable Certificate.3. Machine Learning with Python by IBMMachine Learning with Python teaches the applications of Machine Learning and its applications in the real world using Python. After learning the basics of machine learning, you can move on to its algorithms, such as Regression, Classification, and Clustering algorithms. The course is further classified into six weeks, with each of them targetting on an Introduction to Machine Learning, Regression algorithms including Linear, Non-linear, Simple, and Multiple regression, Classification algorithms including  SVM, Decision Trees, KNN, Logistic Regression, and more. Moreover, the clustering algorithms comprises of Hierarchical Clustering, Partitioned-based Clustering, and Density-based Clustering, Recommender Systems, and the final week inclusive of the Final Project that would implement everything you have learned.4. Machine Learning by HarvardXThis course provides knowledge about the basics of Machine Learning and its various learning algorithms,v principal component analysis, and regularization by developing a movie recommender system. It also provides knowledge about data analysis and training data to receive useful insights. This course majorly focuses on ML algorithms like Linear Regression with One Variable, Linear Regression with Multiple Variables, Logistic Regression, Support Vector Machines, Unsupervised Learning, and a few others. Along with all the ML algorithms, it also teaches you cross-validation to avoid overtraining that data. You can prevail in an instructor-signed certificate from edX, and HarvardX is demonstrating your knowledge of Machine Learning for Data Science and analytics on successful completion of the course.5. Principles of Machine Learning by MicrosoftThis course covers basic principles of Machine Learning that are taught using theory and then followed by practical experiences. These experiences are made by creating and deploying ML models. You can use Python and Azure Notebooks to design these models and procure data insights. Furthermore, this course offers an introduction to Machine Learning and introduces you with data exploration, data preparation, Machine Learning algorithms. These algorithms comprise Supervised & Unsupervised Learning and improving model performance. Same as above, you would receive an instructor-attested certificate from edX and Microsoft to demonstrate your knowledge of Machine Learning for Data Science and Analytics at the end of the course.All the courses mentioned above offer the best machine learning courses from basic to advanced levels.

Machine Learning is the top domain in computer science these days. It is one of the evolving technologies with many organizations in different sectors. This technology can be beneficial for professionals working in various sectors. So, if you’re a professional seeking online courses for machine learning, many platforms provide the material.

You can get to know the basics of Machine Learning from these online portals and then implement these concepts in projects. These courses can help you get a new job or switch careers, or want to learn something new.

Please find some of the best machine learning course provider portals below:

1. Machine Learning by Stanford University

This is one of the most well-known Machine Learning courses available on the web. This course is taught by Andrew Ng, the former Chief Scientist for Baidu and Director of the Google Brain Deep Learning Project. You can learn both the theoretical aspects of Machine Learning algorithms and the practical implementations of it with the help of this course. Moreover, this course provides a full set of Linear Regression with One & Multiple Variable, Logistic Regression, Regularization, Neural Networks, Support Vector Machines, Unsupervised Learning, and many more implementing Octave or MATLAB. It takes about 11 weeks to complete this course, and it covers various aspects and applications of Machine Learning. These learning algorithms can help computer vision, database mining, text-understanding, designing robots, and several other applications. Upon completing this course, you receive a Shareable Certificate that you can attach to your resume or LinkedIn profile.

2. Deep Learning Specialization by DeepLearningAI

Deep Learning Specialization is an advanced specialization course by Andrew NG. You can go for this course after successful completion of the basic Machine Learning course. This course covers various aspects such as Convolutional Networks, Recurrent neural networks, Long short-term memory (LSTM), Natural Language Processing, and numerous others. Moreover, this course also provides personal stories and career advice from many top leaders in Deep Learning, which will enhance your experience. Deep Learning specialization offers five times comprising Neural Networks & Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models. This stream helps you create deep learning models in different fields such as autonomous driving, healthcare, natural language processing, music generation, and many more. Once you complete each of these courses successfully in specialization, you will receive a Shareable Certificate.

3. Machine Learning with Python by IBM

Machine Learning with Python teaches the applications of Machine Learning and its applications in the real world using Python. After learning the basics of machine learning, you can move on to its algorithms, such as Regression, Classification, and Clustering algorithms. The course is further classified into six weeks, with each of them targetting on an Introduction to Machine Learning, Regression algorithms including Linear, Non-linear, Simple, and Multiple regression, Classification algorithms including  SVM, Decision Trees, KNN, Logistic Regression, and more. Moreover, the clustering algorithms comprises of Hierarchical Clustering, Partitioned-based Clustering, and Density-based Clustering, Recommender Systems, and the final week inclusive of the Final Project that would implement everything you have learned.

4. Machine Learning by HarvardX

This course provides knowledge about the basics of Machine Learning and its various learning algorithms,v principal component analysis, and regularization by developing a movie recommender system. It also provides knowledge about data analysis and training data to receive useful insights. This course majorly focuses on ML algorithms like Linear Regression with One Variable, Linear Regression with Multiple Variables, Logistic Regression, Support Vector Machines, Unsupervised Learning, and a few others. Along with all the ML algorithms, it also teaches you cross-validation to avoid overtraining that data. You can prevail in an instructor-signed certificate from edX, and HarvardX is demonstrating your knowledge of Machine Learning for Data Science and analytics on successful completion of the course.

5. Principles of Machine Learning by Microsoft

This course covers basic principles of Machine Learning that are taught using theory and then followed by practical experiences. These experiences are made by creating and deploying ML models. You can use Python and Azure Notebooks to design these models and procure data insights. Furthermore, this course offers an introduction to Machine Learning and introduces you with data exploration, data preparation, Machine Learning algorithms. These algorithms comprise Supervised & Unsupervised Learning and improving model performance. Same as above, you would receive an instructor-attested certificate from edX and Microsoft to demonstrate your knowledge of Machine Learning for Data Science and Analytics at the end of the course.

All the courses mentioned above offer the best machine learning courses from basic to advanced levels.

NodeJS is a platform that enables developers to write server-side high performance and networked applications. And that too using good old Javascript. It uses simple programming syntax and can be used for developing real-time mobile applications. It is a runtime environment based on Chrome’s V8 JavaScript engine. The word “Node” signifies the building blocks of programming code. It can be organized with good networking protocols to communicate with each other and scale up to build large distributed programs. It operates on a single-thread, using non-blocking I/O calls, allowing it to support tens of thousands of concurrent connections held in the event loop. For building an app with node.js, you may need to begin with the following points. Step 1 - Learn basic knowledge to learn/build Node.js web application Basic JavaScript Knowledge Besides Javascript, basic knowledge individuals should be familiar with the latest features such as ES6, Callbacks, Promises, etc. Knowledge in API functionality and different kinds of API like SOAP, REST, etc. Good Knowledge of Network protocols such as HTTP, etc. Knowledge in Web development functionality and the various kinds of frameworks out there that Node JS interacts. Basic knowledge of server-side programming and its applications (withdrawing data from a database for the client, routing and directly serving pages, etc.). Knowledge in either SQL or NoSQL architecture and the related frameworks (such as MySQL, MongoDB, etc). The understanding of the above-mentioned techniques is essential for node.js applications; without that, you won’t be able to program thoroughly. You also need to have a clear concept of functions, scopes, binding, this keyword, a new keyword, closures, classes, module patterns, prototypes, callbacks, promises. Step 2- Learn setting up node.js and supporting functions/programs Once you are clear with the basic Javascript concept, you can follow the following architecture to learn or use Node.js in your app. Node.js Installation Node.js Modules: Node Package Manager (NPM) Http Server and Client: Express Framework, Routes, application deployment Asynchronous Programming: Call stack, Callback queue, and Event Loop mechanism Integration of Node.js and MongoDB: REST APIs and GraphQL Authentication, Application Security and Testing Dynamic Client-Server Interaction and Microservices Application using Node.js You may also need to learn express.js. It is a third-party module used in node js to make server-side code more efficient. It uses different view-engines for front end code and to serve static files on the client-side. If you are planning to build an e-commerce web app, then the MEAN/MERN stack would be the right good choice. You can even deploy a microservices-based app as well. ( Image source: Sinform) Step 3: Explore the best source to learn Node.js Once you are clear with the Node.js content, you can choose any of the given methods to explore your node.js knowledge. There are various ways to learn/develop a web app using Node.js Free/ Paid online course ( Udemy, Udacity, TutorialsTeacher, Pluralsight, Lynda, W3Schools, JavaTpoint, Codeship, Airpair, Coursera,etc.) Node.js community Personal tutorial through webinars Youtube classes (eureka node.js) By working on dummy node.js project under expert supervision Node.js Blogs Free projects you can watch and create Explore quiz and questionnaire on Node.js The self-learning guide and video would be useful for gaining knowledge about Node.Js architecture and its important function. However, if you want to get a good hold on the technology, then working on a node.js project under expert supervision is recommended.
NodeJS is a platform that enables developers to write server-side high performance and networked applications. And that too using good old Javascript. It uses simple programming syntax and can be used for developing real-time mobile applications. It is a runtime environment based on Chrome’s V8 JavaScript engine. The word “Node” signifies the building blocks of programming code. It can be organized with good networking protocols to communicate with each other and scale up to build large distributed programs. It operates on a single-thread, using non-blocking I/O calls, allowing it to support tens of thousands of concurrent connections held in the event loop. For building an app with node.js, you may need to begin with the following points. Step 1 - Learn basic knowledge to learn/build Node.js web application Basic JavaScript Knowledge Besides Javascript, basic knowledge individuals should be familiar with the latest features such as ES6, Callbacks, Promises, etc. Knowledge in API functionality and different kinds of API like SOAP, REST, etc. Good Knowledge of Network protocols such as HTTP, etc. Knowledge in Web development functionality and the various kinds of frameworks out there that Node JS interacts. Basic knowledge of server-side programming and its applications (withdrawing data from a database for the client, routing and directly serving pages, etc.). Knowledge in either SQL or NoSQL architecture and the related frameworks (such as MySQL, MongoDB, etc). The understanding of the above-mentioned techniques is essential for node.js applications; without that, you won’t be able to program thoroughly. You also need to have a clear concept of functions, scopes, binding, this keyword, a new keyword, closures, classes, module patterns, prototypes, callbacks, promises. Step 2- Learn setting up node.js and supporting functions/programs Once you are clear with the basic Javascript concept, you can follow the following architecture to learn or use Node.js in your app. Node.js Installation Node.js Modules: Node Package Manager (NPM) Http Server and Client: Express Framework, Routes, application deployment Asynchronous Programming: Call stack, Callback queue, and Event Loop mechanism Integration of Node.js and MongoDB: REST APIs and GraphQL Authentication, Application Security and Testing Dynamic Client-Server Interaction and Microservices Application using Node.js You may also need to learn express.js. It is a third-party module used in node js to make server-side code more efficient. It uses different view-engines for front end code and to serve static files on the client-side. If you are planning to build an e-commerce web app, then the MEAN/MERN stack would be the right good choice. You can even deploy a microservices-based app as well. ( Image source: Sinform) Step 3: Explore the best source to learn Node.js Once you are clear with the Node.js content, you can choose any of the given methods to explore your node.js knowledge. There are various ways to learn/develop a web app using Node.js Free/ Paid online course ( Udemy, Udacity, TutorialsTeacher, Pluralsight, Lynda, W3Schools, JavaTpoint, Codeship, Airpair, Coursera,etc.) Node.js community Personal tutorial through webinars Youtube classes (eureka node.js) By working on dummy node.js project under expert supervision Node.js Blogs Free projects you can watch and create Explore quiz and questionnaire on Node.js The self-learning guide and video would be useful for gaining knowledge about Node.Js architecture and its important function. However, if you want to get a good hold on the technology, then working on a node.js project under expert supervision is recommended.

NodeJS is a platform that enables developers to write server-side high performance and networked applications. And that too using good old Javascript. It uses simple programming syntax and can be used for developing real-time mobile applications. It is a runtime environment based on Chrome’s V8 JavaScript engine.

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The word “Node” signifies the building blocks of programming code. It can be organized with good networking protocols to communicate with each other and scale up to build large distributed programs. It operates on a single-thread, using non-blocking I/O calls, allowing it to support tens of thousands of concurrent connections held in the event loop.

For building an app with node.js, you may need to begin with the following points.

Step 1 - Learn basic knowledge to learn/build Node.js web application

  1. Basic JavaScript Knowledge
  2. Besides Javascript, basic knowledge individuals should be familiar with the latest features such as ES6, Callbacks, Promises, etc.
  3. Knowledge in API functionality and different kinds of API like SOAP, REST, etc.
  4. Good Knowledge of Network protocols such as HTTP, etc.
  5. Knowledge in Web development functionality and the various kinds of frameworks out there that Node JS interacts.
  6. Basic knowledge of server-side programming and its applications (withdrawing data from a database for the client, routing and directly serving pages, etc.).
  7. Knowledge in either SQL or NoSQL architecture and the related frameworks (such as MySQL, MongoDB, etc).

The understanding of the above-mentioned techniques is essential for node.js applications; without that, you won’t be able to program thoroughly. You also need to have a clear concept of functions, scopes, binding, this keyword, a new keyword, closures, classes, module patterns, prototypes, callbacks, promises.

Step 2- Learn setting up node.js and supporting functions/programs

Once you are clear with the basic Javascript concept, you can follow the following architecture to learn or use Node.js in your app.

  • Node.js Installation
  • Node.js Modules: Node Package Manager (NPM)
  • Http Server and Client: Express Framework, Routes, application deployment
  • Asynchronous Programming: Call stack, Callback queue, and Event Loop mechanism
  • Integration of Node.js and MongoDB: REST APIs and GraphQL
  • Authentication, Application Security and Testing
  • Dynamic Client-Server Interaction and Microservices Application using Node.js

You may also need to learn express.js. It is a third-party module used in node js to make server-side code more efficient. It uses different view-engines for front end code and to serve static files on the client-side. If you are planning to build an e-commerce web app, then the MEAN/MERN stack would be the right good choice. You can even deploy a microservices-based app as well.

undefined

( Image source: Sinform)

Step 3: Explore the best source to learn Node.js

Once you are clear with the Node.js content, you can choose any of the given methods to explore your node.js knowledge.

There are various ways to learn/develop a web app using Node.js

  • Free/ Paid online course ( Udemy, Udacity, TutorialsTeacher, Pluralsight, Lynda, W3Schools, JavaTpoint, Codeship, Airpair, Coursera,etc.)
  • Node.js community
  • Personal tutorial through webinars
  • Youtube classes (eureka node.js)
  • By working on dummy node.js project under expert supervision
  • Node.js Blogs
  • Free projects you can watch and create
  • Explore quiz and questionnaire on Node.js

The self-learning guide and video would be useful for gaining knowledge about Node.Js architecture and its important function. However, if you want to get a good hold on the technology, then working on a node.js project under expert supervision is recommended.

Having been working in the education sector for a quite long time, I want to conclude that learning Management software is now not just another upgrade in the field of education. Although, I personally never felt we would need such advancement as an integral part of learning and training, and that’s because we probably never actually knew what wonders it has in store.There are now many companies coming up with their Advanced LMS for both education and corporate industry. I have been using Teachzy LMS for my educational institute that has a base of around 3000 students and it’s good to know my institute can continue delivering learning services regardless of COVID-19 lockdown.Of late, the situation is screaming for this advancement and I can tell you this is one of the best decisions you will make to grow your education business. I am mentioning the benefits of the LMS app I am using in simple words so that you understand well-Effective overall control of administration and atomizationLIVE communication and interaction without any hassleBetter engagement between trainers and usersFlexible content managementIntegrated payment system within the appLoyalty program to market your institute or organization
Having been working in the education sector for a quite long time, I want to conclude that learning Management software is now not just another upgrade in the field of education. Although, I personally never felt we would need such advancement as an integral part of learning and training, and that’s because we probably never actually knew what wonders it has in store.There are now many companies coming up with their Advanced LMS for both education and corporate industry. I have been using Teachzy LMS for my educational institute that has a base of around 3000 students and it’s good to know my institute can continue delivering learning services regardless of COVID-19 lockdown.Of late, the situation is screaming for this advancement and I can tell you this is one of the best decisions you will make to grow your education business. I am mentioning the benefits of the LMS app I am using in simple words so that you understand well-Effective overall control of administration and atomizationLIVE communication and interaction without any hassleBetter engagement between trainers and usersFlexible content managementIntegrated payment system within the appLoyalty program to market your institute or organization

Having been working in the education sector for a quite long time, I want to conclude that learning Management software is now not just another upgrade in the field of education. Although, I personally never felt we would need such advancement as an integral part of learning and training, and that’s because we probably never actually knew what wonders it has in store.

There are now many companies coming up with their Advanced LMS for both education and corporate industry. I have been using Teachzy LMS for my educational institute that has a base of around 3000 students and it’s good to know my institute can continue delivering learning services regardless of COVID-19 lockdown.

Of late, the situation is screaming for this advancement and I can tell you this is one of the best decisions you will make to grow your education business. I am mentioning the benefits of the LMS app I am using in simple words so that you understand well-

  • Effective overall control of administration and atomization
  • LIVE communication and interaction without any hassle
  • Better engagement between trainers and users
  • Flexible content management
  • Integrated payment system within the app
  • Loyalty program to market your institute or organization
 When you type “the best learning solution” in the search bar, you are often presented with hundreds of options which can be overwhelming. E-learning solutions should be designed by putting learners and teachers at the centre of its development process – it should consider the needs and aptitude of the users.Nothing disappoints a student like software that’s full of glitches or dampens the teachers’ zeal like a solution that is difficult to operate. This is why successful solutions are the ones that satisfy the requirements of its main stakeholders i.e. learners, teachers and developers. In this answer, I will give you a roadmap which will help you think beyond the marketing pitch and choose a solution that truly aligns with your needs. Identify your LMS needsBefore selecting an e-learning solution, the first and foremost step is to learn the context in which you will use the software. Evaluate the maintainability, usability, compatibility and accessibility of the product. Ask yourself these things:Are your learners going to change every month?Will you conduct 4-5 sessions simultaneously?Do you want active participation in sessions through audio/video media?Are the users (trainers and learners)tech-savvy?Do you require technical assistance for your users?Is the workshop option available during a session?Can you conduct mock tests after every training?Can you track the performance report of learners?Do you need an in-built discussion forum?Your e-learning solution should not hinder your training rather it should be an enabler – in seamless delivery. Make a list of features Features are the essential elements of the user experience (UX) in a learning platform. With the advent of new technology and demands – e-learning solutions have also evolved.  Make sure they have integrated the right functionalities to facilitate an enriched experience to users. These features include :Personalisation Tools that keep learners in the loop with the training schedules – calendars, rosters and timetablesInteraction tools (chatbots, annotation and freehand)Tools that manage registration, invitations and attendancePerformance evaluation toolsTest several LMS before purchasing oneThere are two kinds of LMS – one that is directly installed on your server and second is a software as a service (SAAS). Make sure you test the products through free-trials before arriving at the final decision. The storage capacity should be enough to accommodate your requirements. With that, ask-If they have a mobile app for all platformsCan you personalise your URL for your business?Is the content management system robust?Do they give access to API?Can you scale it up in future?Bottom lineE-learning platforms are something that your users and trainers will use for years – don’t make hasty decisions, contemplate the consequences very well. 
 When you type “the best learning solution” in the search bar, you are often presented with hundreds of options which can be overwhelming. E-learning solutions should be designed by putting learners and teachers at the centre of its development process – it should consider the needs and aptitude of the users.Nothing disappoints a student like software that’s full of glitches or dampens the teachers’ zeal like a solution that is difficult to operate. This is why successful solutions are the ones that satisfy the requirements of its main stakeholders i.e. learners, teachers and developers. In this answer, I will give you a roadmap which will help you think beyond the marketing pitch and choose a solution that truly aligns with your needs. Identify your LMS needsBefore selecting an e-learning solution, the first and foremost step is to learn the context in which you will use the software. Evaluate the maintainability, usability, compatibility and accessibility of the product. Ask yourself these things:Are your learners going to change every month?Will you conduct 4-5 sessions simultaneously?Do you want active participation in sessions through audio/video media?Are the users (trainers and learners)tech-savvy?Do you require technical assistance for your users?Is the workshop option available during a session?Can you conduct mock tests after every training?Can you track the performance report of learners?Do you need an in-built discussion forum?Your e-learning solution should not hinder your training rather it should be an enabler – in seamless delivery. Make a list of features Features are the essential elements of the user experience (UX) in a learning platform. With the advent of new technology and demands – e-learning solutions have also evolved.  Make sure they have integrated the right functionalities to facilitate an enriched experience to users. These features include :Personalisation Tools that keep learners in the loop with the training schedules – calendars, rosters and timetablesInteraction tools (chatbots, annotation and freehand)Tools that manage registration, invitations and attendancePerformance evaluation toolsTest several LMS before purchasing oneThere are two kinds of LMS – one that is directly installed on your server and second is a software as a service (SAAS). Make sure you test the products through free-trials before arriving at the final decision. The storage capacity should be enough to accommodate your requirements. With that, ask-If they have a mobile app for all platformsCan you personalise your URL for your business?Is the content management system robust?Do they give access to API?Can you scale it up in future?Bottom lineE-learning platforms are something that your users and trainers will use for years – don’t make hasty decisions, contemplate the consequences very well. 


 

When you type “the best learning solution” in the search bar, you are often presented with hundreds of options which can be overwhelming. E-learning solutions should be designed by putting learners and teachers at the centre of its development process – it should consider the needs and aptitude of the users.

Nothing disappoints a student like software that’s full of glitches or dampens the teachers’ zeal like a solution that is difficult to operate. This is why successful solutions are the ones that satisfy the requirements of its main stakeholders i.e. learners, teachers and developers. 

In this answer, I will give you a roadmap which will help you think beyond the marketing pitch and choose a solution that truly aligns with your needs. 

Identify your LMS needs

Before selecting an e-learning solution, the first and foremost step is to learn the context in which you will use the software. Evaluate the maintainability, usability, compatibility and accessibility of the product. Ask yourself these things:

  • Are your learners going to change every month?
  • Will you conduct 4-5 sessions simultaneously?
  • Do you want active participation in sessions through audio/video media?
  • Are the users (trainers and learners)tech-savvy?
  • Do you require technical assistance for your users?
  • Is the workshop option available during a session?
  • Can you conduct mock tests after every training?
  • Can you track the performance report of learners?
  • Do you need an in-built discussion forum?

Your e-learning solution should not hinder your training rather it should be an enabler – in seamless delivery. 

Make a list of features 

Features are the essential elements of the user experience (UX) in a learning platform. With the advent of new technology and demands – e-learning solutions have also evolved.  Make sure they have integrated the right functionalities to facilitate an enriched experience to users. These features include :

  • Personalisation 
  • Tools that keep learners in the loop with the training schedules – calendars, rosters and timetables
  • Interaction tools (chatbots, annotation and freehand)
  • Tools that manage registration, invitations and attendance
  • Performance evaluation tools

Test several LMS before purchasing one

There are two kinds of LMS – one that is directly installed on your server and second is a software as a service (SAAS). Make sure you test the products through free-trials before arriving at the final decision. The storage capacity should be enough to accommodate your requirements. With that, ask-

  • If they have a mobile app for all platforms
  • Can you personalise your URL for your business?
  • Is the content management system robust?
  • Do they give access to API?
  • Can you scale it up in future?

Bottom line

E-learning platforms are something that your users and trainers will use for years – don’t make hasty decisions, contemplate the consequences very well. 

Artificial Intelligence and Machine Learning are a vital constituent of computer science and are co-related to each other. Today, these two are the most trending technologies and are widely used in developing intelligent systems. Artificial Intelligence makes a computer system that can mimic human intelligence. Its system does not require being pre-programmed. Instead, it uses the algorithms that can work with their own intelligence. Google’s AlphaGo, Siri, and AI in Chess Playing are an example of Artificial-enabled intelligent systems. Machine Learning involves knowledge extraction from the data. As a sub-field of Artificial Intelligence, Machine Learning enables the machines to learn from historical data and experiences without being explicitly programmed. Now, coming to the answer, I assume that you are well-versed in some of the OOP languages like Java, C++, Python, as you said you are good in machine learning. Don’t jump into AI directly as it would mean to focus everything under the AI umbrella, which is not actually possible. Check the image given below to understand the relation between AI and its sub-heads better. There is a lot you need to know before jumping to AI. Learn ML as hard as you can. Then make your way to Deep Learning that is a little advanced ML. It sets up basic parameters about the data and trains the computer to learn the things on its own using pattern recognition technique and many other layers of processing. Though we can achieve Artificial Intelligence without using Machine Learning concepts, it would require millions of lines of code. So, ML principles are used to minimize code complexity and LOC. This way, the algorithm adjusts and improve themselves. AI is the best, and ML is the path to achieve AI. Learning AI would definitely help to improve your concepts related to Machine Learning.
Artificial Intelligence and Machine Learning are a vital constituent of computer science and are co-related to each other. Today, these two are the most trending technologies and are widely used in developing intelligent systems. Artificial Intelligence makes a computer system that can mimic human intelligence. Its system does not require being pre-programmed. Instead, it uses the algorithms that can work with their own intelligence. Google’s AlphaGo, Siri, and AI in Chess Playing are an example of Artificial-enabled intelligent systems. Machine Learning involves knowledge extraction from the data. As a sub-field of Artificial Intelligence, Machine Learning enables the machines to learn from historical data and experiences without being explicitly programmed. Now, coming to the answer, I assume that you are well-versed in some of the OOP languages like Java, C++, Python, as you said you are good in machine learning. Don’t jump into AI directly as it would mean to focus everything under the AI umbrella, which is not actually possible. Check the image given below to understand the relation between AI and its sub-heads better. There is a lot you need to know before jumping to AI. Learn ML as hard as you can. Then make your way to Deep Learning that is a little advanced ML. It sets up basic parameters about the data and trains the computer to learn the things on its own using pattern recognition technique and many other layers of processing. Though we can achieve Artificial Intelligence without using Machine Learning concepts, it would require millions of lines of code. So, ML principles are used to minimize code complexity and LOC. This way, the algorithm adjusts and improve themselves. AI is the best, and ML is the path to achieve AI. Learning AI would definitely help to improve your concepts related to Machine Learning.

Artificial Intelligence and Machine Learning are a vital constituent of computer science and are co-related to each other. Today, these two are the most trending technologies and are widely used in developing intelligent systems.

Artificial Intelligence makes a computer system that can mimic human intelligence. Its system does not require being pre-programmed. Instead, it uses the algorithms that can work with their own intelligence. Google’s AlphaGo, Siri, and AI in Chess Playing are an example of Artificial-enabled intelligent systems.

Machine Learning involves knowledge extraction from the data. As a sub-field of Artificial Intelligence, Machine Learning enables the machines to learn from historical data and experiences without being explicitly programmed.

Now, coming to the answer, I assume that you are well-versed in some of the OOP languages like Java, C++, Python, as you said you are good in machine learning.

Don’t jump into AI directly as it would mean to focus everything under the AI umbrella, which is not actually possible.

Check the image given below to understand the relation between AI and its sub-heads better.

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There is a lot you need to know before jumping to AI. Learn ML as hard as you can. Then make your way to Deep Learning that is a little advanced ML. It sets up basic parameters about the data and trains the computer to learn the things on its own using pattern recognition technique and many other layers of processing.

Though we can achieve Artificial Intelligence without using Machine Learning concepts, it would require millions of lines of code. So, ML principles are used to minimize code complexity and LOC. This way, the algorithm adjusts and improve themselves.

AI is the best, and ML is the path to achieve AI. Learning AI would definitely help to improve your concepts related to Machine Learning.

What are some good digital learning platforms for education?The digital learning platform has brought significant change in the educational sector. These days, you will find that digital learning platforms exist in all educational organizations like schools, universities, and training institutions.What is a digital learning platform?Digital learning platforms serve as the technology that helps the educational process for smooth and efficient functioning. This platform includes various systems like – learning management systems (LMS), Virtual learning environments (VLE), and learning content management systems (LCMS).There are lots of digital learning software applications that can work as a blessing for the education department. It is worth investing in an excellent digital learning platform so that you can carry out the educational activities seamlessly.Let’s move forward to explore the top 5 digital learning applications that can make the work of users and organizations efficient and flawless.Top 5 digital learning software1 Pocket StudyIt is a unique learning digital platform that can help in sharing the learning resources competently. This quick-learning app keeps teachers connected with the students. Through flashcards, the students can experience a new way of learning. In the most organized manner, the students can access the content of the entire syllabus. Teachers can freely and easily create content for the students. With mobile app technology, the user can enjoy digital learning.Highlights:User ManagementAcademic/EducationMobile learningLearner PortalCurriculum ManagementMultilingualAlert/ NotificationsContent PublishingCustomizable TemplatesDocument ManagementAnalyticsMedia ManagementDeployment – Cloud Hosted,    Pricing Model – Annual Subscription, Quote BasedBusiness Size – SML Freelancer  2 Docebo This high-quality software has changed the way people learn by using the technique of artificial intelligence. You can drive growth, organizational performance, and revenue in your business with this learning platform's help. In the process of learning, this e-learning tool facilitates automated and personalized learning experiences.Highlights:Blended LearningPersonalized CourseSCORM ComplianceUser FeedbackCurriculum ManagementCourse LibraryLearner PortalMultilingualAsynchronous LearningDeployment: Cloud HostedPricing Model: Monthly PaymentBusiness Size: SML3 Blackboard Open LMSIf you are looking to drive institutional and organizational effectiveness, then Blackboard Open LMS is apt for you. This digital learning application provides an open-source and robust foundation for online and professional learning. It offers remarkable results for competency-based blended learning.Highlights:Academic/EducationCorporate/BusinessPersonalized CourseDeployment: Cloud Hosted, Open APIPricing Model: Quote BasedBusiness Size: SM4 WizIQThis software delivers a great learning experience to the users. It is equipped with a high-definition virtual classroom. This digital learning tool has a cloud-based course builder with infinite course creation and upload capacity. This platform is feature-rich that provides end-to-end online learning delivery solutions to the users.Highlights:Blended learningSCORM ComplianceVirtual ClassroomTests and AssessmentsCourse BuilderReports and AnalyticsLMS eCommerceMobile LearningDeployment: Cloud HostedPricing Model: Monthly PaymentBusiness Size: SML5 Adobe Capitvate Prime LMSThis digital learning platform delivers modern learning experiences to its users. It provides an AI-based social learning platform for the learners that help in encouraging a collaborative learning culture. Peers can quickly learn from each other with this software. This application lessens the skill gap to manage a learner ecosystem that is engaged and updated.Highlights:Academic/EducationBlended LearningCorporate/BusinessSCORM ComplianceTests & AssessmentsUser FeedbackBrand new reporting dashboardsCourse-feedback reportsExtendable Social LearningAutomated Content CurationDiscussion BoardsDeployment: Cloud HostedPricing Model: Monthly Payment, Quote BasedBusiness Size: SM FreelancerConclusive statementDigital learning platform is useful not only for students but for teachers too. The academic institutions and businesses have recognized that a lot more can be achieved from the digital learning platform. In the education sector, the digital learning platform has become increasingly common. All these digital learning resources are reasonable and easy-to-use.If you are looking for more advanced digital learning features, then explore the links below:Best Learning Management System (LMS) Software in 2020 | GoodFirmsBest E-Learning Software - Reviews 2020 | GoodFirms
What are some good digital learning platforms for education?The digital learning platform has brought significant change in the educational sector. These days, you will find that digital learning platforms exist in all educational organizations like schools, universities, and training institutions.What is a digital learning platform?Digital learning platforms serve as the technology that helps the educational process for smooth and efficient functioning. This platform includes various systems like – learning management systems (LMS), Virtual learning environments (VLE), and learning content management systems (LCMS).There are lots of digital learning software applications that can work as a blessing for the education department. It is worth investing in an excellent digital learning platform so that you can carry out the educational activities seamlessly.Let’s move forward to explore the top 5 digital learning applications that can make the work of users and organizations efficient and flawless.Top 5 digital learning software1 Pocket StudyIt is a unique learning digital platform that can help in sharing the learning resources competently. This quick-learning app keeps teachers connected with the students. Through flashcards, the students can experience a new way of learning. In the most organized manner, the students can access the content of the entire syllabus. Teachers can freely and easily create content for the students. With mobile app technology, the user can enjoy digital learning.Highlights:User ManagementAcademic/EducationMobile learningLearner PortalCurriculum ManagementMultilingualAlert/ NotificationsContent PublishingCustomizable TemplatesDocument ManagementAnalyticsMedia ManagementDeployment – Cloud Hosted,    Pricing Model – Annual Subscription, Quote BasedBusiness Size – SML Freelancer  2 Docebo This high-quality software has changed the way people learn by using the technique of artificial intelligence. You can drive growth, organizational performance, and revenue in your business with this learning platform's help. In the process of learning, this e-learning tool facilitates automated and personalized learning experiences.Highlights:Blended LearningPersonalized CourseSCORM ComplianceUser FeedbackCurriculum ManagementCourse LibraryLearner PortalMultilingualAsynchronous LearningDeployment: Cloud HostedPricing Model: Monthly PaymentBusiness Size: SML3 Blackboard Open LMSIf you are looking to drive institutional and organizational effectiveness, then Blackboard Open LMS is apt for you. This digital learning application provides an open-source and robust foundation for online and professional learning. It offers remarkable results for competency-based blended learning.Highlights:Academic/EducationCorporate/BusinessPersonalized CourseDeployment: Cloud Hosted, Open APIPricing Model: Quote BasedBusiness Size: SM4 WizIQThis software delivers a great learning experience to the users. It is equipped with a high-definition virtual classroom. This digital learning tool has a cloud-based course builder with infinite course creation and upload capacity. This platform is feature-rich that provides end-to-end online learning delivery solutions to the users.Highlights:Blended learningSCORM ComplianceVirtual ClassroomTests and AssessmentsCourse BuilderReports and AnalyticsLMS eCommerceMobile LearningDeployment: Cloud HostedPricing Model: Monthly PaymentBusiness Size: SML5 Adobe Capitvate Prime LMSThis digital learning platform delivers modern learning experiences to its users. It provides an AI-based social learning platform for the learners that help in encouraging a collaborative learning culture. Peers can quickly learn from each other with this software. This application lessens the skill gap to manage a learner ecosystem that is engaged and updated.Highlights:Academic/EducationBlended LearningCorporate/BusinessSCORM ComplianceTests & AssessmentsUser FeedbackBrand new reporting dashboardsCourse-feedback reportsExtendable Social LearningAutomated Content CurationDiscussion BoardsDeployment: Cloud HostedPricing Model: Monthly Payment, Quote BasedBusiness Size: SM FreelancerConclusive statementDigital learning platform is useful not only for students but for teachers too. The academic institutions and businesses have recognized that a lot more can be achieved from the digital learning platform. In the education sector, the digital learning platform has become increasingly common. All these digital learning resources are reasonable and easy-to-use.If you are looking for more advanced digital learning features, then explore the links below:Best Learning Management System (LMS) Software in 2020 | GoodFirmsBest E-Learning Software - Reviews 2020 | GoodFirms

What are some good digital learning platforms for education?

The digital learning platform has brought significant change in the educational sector. These days, you will find that digital learning platforms exist in all educational organizations like schools, universities, and training institutions.

What is a digital learning platform?

Digital learning platforms serve as the technology that helps the educational process for smooth and efficient functioning. This platform includes various systems like – learning management systems (LMS), Virtual learning environments (VLE), and learning content management systems (LCMS).

There are lots of digital learning software applications that can work as a blessing for the education department. It is worth investing in an excellent digital learning platform so that you can carry out the educational activities seamlessly.

Let’s move forward to explore the top 5 digital learning applications that can make the work of users and organizations efficient and flawless.

Top 5 digital learning software

Pocket Study

It is a unique learning digital platform that can help in sharing the learning resources competently. This quick-learning app keeps teachers connected with the students. Through flashcards, the students can experience a new way of learning. In the most organized manner, the students can access the content of the entire syllabus. Teachers can freely and easily create content for the students. With mobile app technology, the user can enjoy digital learning.

Highlights:

  • User Management
  • Academic/Education
  • Mobile learning
  • Learner Portal
  • Curriculum Management
  • Multilingual
  • Alert/ Notifications
  • Content Publishing
  • Customizable Templates
  • Document Management
  • Analytics
  • Media Management

Deployment – Cloud Hosted,    

Pricing Model – Annual Subscription, Quote Based

Business Size – SML Freelancer 

 Docebo 

This high-quality software has changed the way people learn by using the technique of artificial intelligence. You can drive growth, organizational performance, and revenue in your business with this learning platform's help. In the process of learning, this e-learning tool facilitates automated and personalized learning experiences.

Highlights:

  • Blended Learning
  • Personalized Course
  • SCORM Compliance
  • User Feedback
  • Curriculum Management
  • Course Library
  • Learner Portal
  • Multilingual
  • Asynchronous Learning

Deployment: Cloud Hosted

Pricing Model: Monthly Payment

Business Size: SML

Blackboard Open LMS

If you are looking to drive institutional and organizational effectiveness, then Blackboard Open LMS is apt for you. This digital learning application provides an open-source and robust foundation for online and professional learning. It offers remarkable results for competency-based blended learning.

Highlights:

  • Academic/Education
  • Corporate/Business
  • Personalized Course

DeploymentCloud Hosted, Open API

Pricing ModelQuote Based

Business Size: SM

WizIQ

This software delivers a great learning experience to the users. It is equipped with a high-definition virtual classroom. This digital learning tool has a cloud-based course builder with infinite course creation and upload capacity. This platform is feature-rich that provides end-to-end online learning delivery solutions to the users.

Highlights:

  • Blended learning
  • SCORM Compliance
  • Virtual Classroom
  • Tests and Assessments
  • Course Builder
  • Reports and Analytics
  • LMS eCommerce
  • Mobile Learning

Deployment: Cloud Hosted

Pricing Model: Monthly Payment

Business Size: SML

Adobe Capitvate Prime LMS

This digital learning platform delivers modern learning experiences to its users. It provides an AI-based social learning platform for the learners that help in encouraging a collaborative learning culture. Peers can quickly learn from each other with this software. This application lessens the skill gap to manage a learner ecosystem that is engaged and updated.

Highlights:

  • Academic/Education
  • Blended Learning
  • Corporate/Business
  • SCORM Compliance
  • Tests & Assessments
  • User Feedback
  • Brand new reporting dashboards
  • Course-feedback reports
  • Extendable Social Learning
  • Automated Content Curation
  • Discussion Boards

Deployment: Cloud Hosted

Pricing Model: Monthly Payment, Quote Based

Business Size: SM Freelancer

Conclusive statement

Digital learning platform is useful not only for students but for teachers too. The academic institutions and businesses have recognized that a lot more can be achieved from the digital learning platform. In the education sector, the digital learning platform has become increasingly common. All these digital learning resources are reasonable and easy-to-use.

If you are looking for more advanced digital learning features, then explore the links below:

Best Learning Management System (LMS) Software in 2020 | GoodFirms

Best E-Learning Software - Reviews 2020 | GoodFirms

The advent of advanced technological breakthroughs such as Artificial Intelligence(AI), Cloud Computing, Virtual Reality(VR) and Augmented Reality(AR) are now shaping the future of industries and transforming human experience worldwide. The internet transitioned from being a curiosity to being a fundamental element of everything that we do today – somehow, it’s all connected to the web.Nowadays, these newly emerged technologies also feel strange but the pace at which they are penetrating through markets and widening their applications in every field – they are soon going to be part of our everyday conversation. One of such technologies is Machine Learning – a subset of AI. In simple terms – ML is predicting results based on the incoming data (manual or automated). Let’s learn how it is transforming lives and industries;It enhances business intelligenceWith advanced tools of BI, machine learning helps simplify the complex data and provides patterns which help businesses make informed decisions. It significantly reduces the “mental labour” of humans and helps them invest their time in important matters of business. It makes the processes and operations more productive and meaningful. It makes life convenient, secure and smartRemember the good old “Google Maps”? It has now made navigating locations so easy with real-time information on traffic, construction works, hurdles and the best route. And with IoT devices and sensors - ML is expanding the capabilities of your appliances, properties and businesses. The appliances are smart enough to make decisions in your absence and even save your house from catastrophic conditions. They can order inventory for you if they see your refrigerator is empty and notify you if they suspect burglary. Environmental protectionBecause of climatic patterns and insightful data from thousands of sources, ecological experts can now make efficient pollution and weather forecasts. The big data and stats help find solutions to previously undefendable issues. Improved healthcareA human brain cannot process huge amounts of data, it is both time-consuming and not as efficient as machines. To combat that, healthcare centres across the globe have started using AI and ML to process enormous dataset from various sources. This way, they get reliable clinical insights required for treatment, care and medicine innovations. The outstanding online shopping experience Machine learning makes e-commerce websites more intuitive and interactive. They help map out the journey of each customer, find out the hurdles in the sales funnel, solve customers’ problems in real-time and make smart recommendations. All things consideredIn the future, you will witness the applications of machine learning in every aspect of business and home. 
The advent of advanced technological breakthroughs such as Artificial Intelligence(AI), Cloud Computing, Virtual Reality(VR) and Augmented Reality(AR) are now shaping the future of industries and transforming human experience worldwide. The internet transitioned from being a curiosity to being a fundamental element of everything that we do today – somehow, it’s all connected to the web.Nowadays, these newly emerged technologies also feel strange but the pace at which they are penetrating through markets and widening their applications in every field – they are soon going to be part of our everyday conversation. One of such technologies is Machine Learning – a subset of AI. In simple terms – ML is predicting results based on the incoming data (manual or automated). Let’s learn how it is transforming lives and industries;It enhances business intelligenceWith advanced tools of BI, machine learning helps simplify the complex data and provides patterns which help businesses make informed decisions. It significantly reduces the “mental labour” of humans and helps them invest their time in important matters of business. It makes the processes and operations more productive and meaningful. It makes life convenient, secure and smartRemember the good old “Google Maps”? It has now made navigating locations so easy with real-time information on traffic, construction works, hurdles and the best route. And with IoT devices and sensors - ML is expanding the capabilities of your appliances, properties and businesses. The appliances are smart enough to make decisions in your absence and even save your house from catastrophic conditions. They can order inventory for you if they see your refrigerator is empty and notify you if they suspect burglary. Environmental protectionBecause of climatic patterns and insightful data from thousands of sources, ecological experts can now make efficient pollution and weather forecasts. The big data and stats help find solutions to previously undefendable issues. Improved healthcareA human brain cannot process huge amounts of data, it is both time-consuming and not as efficient as machines. To combat that, healthcare centres across the globe have started using AI and ML to process enormous dataset from various sources. This way, they get reliable clinical insights required for treatment, care and medicine innovations. The outstanding online shopping experience Machine learning makes e-commerce websites more intuitive and interactive. They help map out the journey of each customer, find out the hurdles in the sales funnel, solve customers’ problems in real-time and make smart recommendations. All things consideredIn the future, you will witness the applications of machine learning in every aspect of business and home. 

The advent of advanced technological breakthroughs such as Artificial Intelligence(AI), Cloud Computing, Virtual Reality(VR) and Augmented Reality(AR) are now shaping the future of industries and transforming human experience worldwide. The internet transitioned from being a curiosity to being a fundamental element of everything that we do today – somehow, it’s all connected to the web.

Nowadays, these newly emerged technologies also feel strange but the pace at which they are penetrating through markets and widening their applications in every field – they are soon going to be part of our everyday conversation. One of such technologies is Machine Learning – a subset of AI. In simple terms – ML is predicting results based on the incoming data (manual or automated). 

Let’s learn how it is transforming lives and industries;

It enhances business intelligence

With advanced tools of BI, machine learning helps simplify the complex data and provides patterns which help businesses make informed decisions. It significantly reduces the “mental labour” of humans and helps them invest their time in important matters of business. It makes the processes and operations more productive and meaningful. 

It makes life convenient, secure and smart

Remember the good old “Google Maps”? It has now made navigating locations so easy with real-time information on traffic, construction works, hurdles and the best route. And with IoT devices and sensors - ML is expanding the capabilities of your appliances, properties and businesses. 

The appliances are smart enough to make decisions in your absence and even save your house from catastrophic conditions. They can order inventory for you if they see your refrigerator is empty and notify you if they suspect burglary. 

Environmental protection

Because of climatic patterns and insightful data from thousands of sources, ecological experts can now make efficient pollution and weather forecasts. The big data and stats help find solutions to previously undefendable issues. 

Improved healthcare

A human brain cannot process huge amounts of data, it is both time-consuming and not as efficient as machines. To combat that, healthcare centres across the globe have started using AI and ML to process enormous dataset from various sources. This way, they get reliable clinical insights required for treatment, care and medicine innovations. 

The outstanding online shopping experience 

Machine learning makes e-commerce websites more intuitive and interactive. They help map out the journey of each customer, find out the hurdles in the sales funnel, solve customers’ problems in real-time and make smart recommendations. 

All things considered

In the future, you will witness the applications of machine learning in every aspect of business and home. 

 The pandemic, COVID-19 outbreak has disrupted the tutorial year, canceled classes, and examinations across the country. The lethal virus has led to tectonic shifts in almost every aspect of our lives be it the sphere of business, entertainment, hospitality, or education – and we are still grappling with the repercussions of those radical changes. Soon after it became clear that this was no passing virus, educational institutions started the move towards online teaching – and students and professors adapted rather quickly to the changing methods of teaching, learning, and grading. To make sure that THE students don't miss out on their studies, schools moved to the concept of classes online, forcing students to attend lectures via their gadgets. The reality is that the longer-term isn't so black and white anymore – there’s tons of grey in between. However, when it involves the talk of 1 over the opposite specifically for school education, the reality is that online learning can never really replace an on-campus experience. Here’s why.·       A teacher-student relationship is often best established within the classroom only. The interaction and therefore the questions a student asks the trainer within the class cannot happen in online interaction. It is true that virtual learning offers a substitute to classroom learning within the time of emergency but it cannot replace the classroom forever.·       Classroom teaching will always be in fashion and given more preference over virtual classes. Daily interactions with teachers and peers improve their skills to affect and teach them the way to sleep in a society. Moreover, schools organize competitions and exams which help in shaping and molding the skills that are helpful for day-to-day life.·       Being in school, as an example, and asking an issue teaches you particular confidence, with debating and discussing ideas spontaneously rather than typing in anonymous questions. Not being on campus takes away the post-class discussions – arguing with a lover over dinner a few points made in school. It takes away the power of scholars to return together, engage, discuss, and make bonds with each other.·       Digital learning also takes away another crucial aspect of campus life – the chance to seek out new interests and learn new skills by joining clubs and societies. Students sitting reception cannot participate in events like performances, competitions, fairs, and mixers that allow them to explore new fields, and develop other aspects of their personalities.While it's going to be easy to adapt to online learning for mainstream theoretical subjects, that's not the case for an array of disciplines and fields that students seek to concentrate on. Online learning takes away valuable resources like lab facilities and studios -resources that are essential for a few subjects and can't be recreated reception.
 The pandemic, COVID-19 outbreak has disrupted the tutorial year, canceled classes, and examinations across the country. The lethal virus has led to tectonic shifts in almost every aspect of our lives be it the sphere of business, entertainment, hospitality, or education – and we are still grappling with the repercussions of those radical changes. Soon after it became clear that this was no passing virus, educational institutions started the move towards online teaching – and students and professors adapted rather quickly to the changing methods of teaching, learning, and grading. To make sure that THE students don't miss out on their studies, schools moved to the concept of classes online, forcing students to attend lectures via their gadgets. The reality is that the longer-term isn't so black and white anymore – there’s tons of grey in between. However, when it involves the talk of 1 over the opposite specifically for school education, the reality is that online learning can never really replace an on-campus experience. Here’s why.·       A teacher-student relationship is often best established within the classroom only. The interaction and therefore the questions a student asks the trainer within the class cannot happen in online interaction. It is true that virtual learning offers a substitute to classroom learning within the time of emergency but it cannot replace the classroom forever.·       Classroom teaching will always be in fashion and given more preference over virtual classes. Daily interactions with teachers and peers improve their skills to affect and teach them the way to sleep in a society. Moreover, schools organize competitions and exams which help in shaping and molding the skills that are helpful for day-to-day life.·       Being in school, as an example, and asking an issue teaches you particular confidence, with debating and discussing ideas spontaneously rather than typing in anonymous questions. Not being on campus takes away the post-class discussions – arguing with a lover over dinner a few points made in school. It takes away the power of scholars to return together, engage, discuss, and make bonds with each other.·       Digital learning also takes away another crucial aspect of campus life – the chance to seek out new interests and learn new skills by joining clubs and societies. Students sitting reception cannot participate in events like performances, competitions, fairs, and mixers that allow them to explore new fields, and develop other aspects of their personalities.While it's going to be easy to adapt to online learning for mainstream theoretical subjects, that's not the case for an array of disciplines and fields that students seek to concentrate on. Online learning takes away valuable resources like lab facilities and studios -resources that are essential for a few subjects and can't be recreated reception.

 

The pandemic, COVID-19 outbreak has disrupted the tutorial year, canceled classes, and examinations across the country. The lethal virus has led to tectonic shifts in almost every aspect of our lives be it the sphere of business, entertainment, hospitality, or education – and we are still grappling with the repercussions of those radical changes. 

Soon after it became clear that this was no passing virus, educational institutions started the move towards online teaching – and students and professors adapted rather quickly to the changing methods of teaching, learning, and grading. To make sure that THE students don't miss out on their studies, schools moved to the concept of classes online, forcing students to attend lectures via their gadgets. The reality is that the longer-term isn't so black and white anymore – there’s tons of grey in between. However, when it involves the talk of 1 over the opposite specifically for school education, the reality is that online learning can never really replace an on-campus experience.

 

Here’s why.

·       A teacher-student relationship is often best established within the classroom only. The interaction and therefore the questions a student asks the trainer within the class cannot happen in online interaction. It is true that virtual learning offers a substitute to classroom learning within the time of emergency but it cannot replace the classroom forever.

·       Classroom teaching will always be in fashion and given more preference over virtual classes. Daily interactions with teachers and peers improve their skills to affect and teach them the way to sleep in a society. Moreover, schools organize competitions and exams which help in shaping and molding the skills that are helpful for day-to-day life.

·       Being in school, as an example, and asking an issue teaches you particular confidence, with debating and discussing ideas spontaneously rather than typing in anonymous questions. Not being on campus takes away the post-class discussions – arguing with a lover over dinner a few points made in school. It takes away the power of scholars to return together, engage, discuss, and make bonds with each other.

·       Digital learning also takes away another crucial aspect of campus life – the chance to seek out new interests and learn new skills by joining clubs and societies. Students sitting reception cannot participate in events like performances, competitions, fairs, and mixers that allow them to explore new fields, and develop other aspects of their personalities.

While it's going to be easy to adapt to online learning for mainstream theoretical subjects, that's not the case for an array of disciplines and fields that students seek to concentrate on. Online learning takes away valuable resources like lab facilities and studios -resources that are essential for a few subjects and can't be recreated reception.

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