Get Up and LEARN

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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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34 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.

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
If you ask any programming wizard the secret to skilled programming, I can assure he will respond with this “ I am not a great programmer; I am just a good programmer with great habits.” It means writing clean code with minimum complexity. It applies to any software programs regardless of what technology the program is developed.Machine learning and data science involve applying some most complex logic.  For some programmers, it is still a puzzle to envisage simple coding structure and machine learning on the same page. However, with best practice, the code complexity in machine learning and data science projects can be minimized.  Ways to reduce code complexity in machine learning and data science projects1)  Implement clean code practiceThere is a well-known phrase among programmers “we will read more code than we will ever write”. Cluttered code makes it difficult to understand and modify. One common issue is keeping “dead code”  in the codebase. Removing dead code gives a clear visual of your code. Try to avoid print statements (even glorified print statements such as head(),df.describe(), df.plot() should be avoided).2)  Variable namesIn software programming, it is a bad practice to embed information about variable types in the variable name. One common problem in data science code is data frames (df). Every data frame is named as df. The good programming practice says, don’t force the reader of your code to translate what the variable means. Instead, try to use a naming convention that is easy to understand. See the example below, with Pandas Python library, the code is written in much human-readable form ( Code 2), and it gives a better idea about what the code is about compared to code 1. ( image source: towardsdatascience)Code (the same above code) declared with a name instead of “df”.  If you compare the above code with the below code, you can quickly figure out which one is easy to understand. ( image source: towardsdatascience)3)  Use functions to simplify code Function simplifies the code usage.  If ‘function” is declared wisely, then programmers don’t have to read each line or search the internet for things that they don’t understand.  Also, to repeat the same transformation on any column, a single line of code would do the job instead of writing lines and lines of code. 4)  Take code out of Jupyter notebooksJupyter notebooks are great for quick prototyping. This is where developer — glue code, , glorified print statements (df.describe() or df.plot()), print statements, unused import statements and even stack traces. But it can make things more complicated as everything gets accumulated in one place. So, whenever a single line of code is changed, the entire notebook has to re-run. It means the longer the notebooks become, the harder it is to get feedback on whether our changes are working. The alternate way is to extract code into functions and Python modules, and have unit tests. With unit tests, the test runner can give feedback on the changes in a matter of seconds, even when there are hundreds of functions.5)  Adopt a test-driven development approachUnlike traditional software development, in test-driven development, you write a test for your code first, make sure it fails, and then create enough code to pass the test.  It means you are only focussed on passing the actual test and not writing code that is intended to pass all possible tests in the future. Once the developer has the code that passes the test, they can clean up the code, remove any duplication, apply good coding style rules, and add documentation. The TDD approach yields short cycles of fail-pass iterations. In the above points, we have mentioned the importance of functions, and the TDD cycle enables the programmer to write functions in small modules.6)  Make small and frequent commitsThe whole development process can greatly benefit from small and frequent commits. Often large changes in code can be difficult to review and takes a significant amount of energy. With small commits, the reviewer can detect early whenever a developer is going in the wrong direction. It also helps to reduce visual distractions and cognitive load.
If you ask any programming wizard the secret to skilled programming, I can assure he will respond with this “ I am not a great programmer; I am just a good programmer with great habits.” It means writing clean code with minimum complexity. It applies to any software programs regardless of what technology the program is developed.Machine learning and data science involve applying some most complex logic.  For some programmers, it is still a puzzle to envisage simple coding structure and machine learning on the same page. However, with best practice, the code complexity in machine learning and data science projects can be minimized.  Ways to reduce code complexity in machine learning and data science projects1)  Implement clean code practiceThere is a well-known phrase among programmers “we will read more code than we will ever write”. Cluttered code makes it difficult to understand and modify. One common issue is keeping “dead code”  in the codebase. Removing dead code gives a clear visual of your code. Try to avoid print statements (even glorified print statements such as head(),df.describe(), df.plot() should be avoided).2)  Variable namesIn software programming, it is a bad practice to embed information about variable types in the variable name. One common problem in data science code is data frames (df). Every data frame is named as df. The good programming practice says, don’t force the reader of your code to translate what the variable means. Instead, try to use a naming convention that is easy to understand. See the example below, with Pandas Python library, the code is written in much human-readable form ( Code 2), and it gives a better idea about what the code is about compared to code 1. ( image source: towardsdatascience)Code (the same above code) declared with a name instead of “df”.  If you compare the above code with the below code, you can quickly figure out which one is easy to understand. ( image source: towardsdatascience)3)  Use functions to simplify code Function simplifies the code usage.  If ‘function” is declared wisely, then programmers don’t have to read each line or search the internet for things that they don’t understand.  Also, to repeat the same transformation on any column, a single line of code would do the job instead of writing lines and lines of code. 4)  Take code out of Jupyter notebooksJupyter notebooks are great for quick prototyping. This is where developer — glue code, , glorified print statements (df.describe() or df.plot()), print statements, unused import statements and even stack traces. But it can make things more complicated as everything gets accumulated in one place. So, whenever a single line of code is changed, the entire notebook has to re-run. It means the longer the notebooks become, the harder it is to get feedback on whether our changes are working. The alternate way is to extract code into functions and Python modules, and have unit tests. With unit tests, the test runner can give feedback on the changes in a matter of seconds, even when there are hundreds of functions.5)  Adopt a test-driven development approachUnlike traditional software development, in test-driven development, you write a test for your code first, make sure it fails, and then create enough code to pass the test.  It means you are only focussed on passing the actual test and not writing code that is intended to pass all possible tests in the future. Once the developer has the code that passes the test, they can clean up the code, remove any duplication, apply good coding style rules, and add documentation. The TDD approach yields short cycles of fail-pass iterations. In the above points, we have mentioned the importance of functions, and the TDD cycle enables the programmer to write functions in small modules.6)  Make small and frequent commitsThe whole development process can greatly benefit from small and frequent commits. Often large changes in code can be difficult to review and takes a significant amount of energy. With small commits, the reviewer can detect early whenever a developer is going in the wrong direction. It also helps to reduce visual distractions and cognitive load.

If you ask any programming wizard the secret to skilled programming, I can assure he will respond with this “ I am not a great programmer; I am just a good programmer with great habits.” It means writing clean code with minimum complexity. It applies to any software programs regardless of what technology the program is developed.

Machine learning and data science involve applying some most complex logic.  For some programmers, it is still a puzzle to envisage simple coding structure and machine learning on the same page. However, with best practice, the code complexity in machine learning and data science projects can be minimized. 

 

Ways to reduce code complexity in machine learning and data science projects

1)  Implement clean code practice

There is a well-known phrase among programmers “we will read more code than we will ever write”. Cluttered code makes it difficult to understand and modify. One common issue is keeping “dead code”  in the codebase. Removing dead code gives a clear visual of your code. Try to avoid print statements (even glorified print statements such as head(),df.describe(), df.plot() should be avoided).

2)  Variable names

In software programming, it is a bad practice to embed information about variable types in the variable name. One common problem in data science code is data frames (df). Every data frame is named as df. The good programming practice says, don’t force the reader of your code to translate what the variable means. Instead, try to use a naming convention that is easy to understand. See the example below, with Pandas Python library, the code is written in much human-readable form ( Code 2), and it gives a better idea about what the code is about compared to code 1.

 

( image source: towardsdatascience)

Code (the same above code) declared with a name instead of “df”.  If you compare the above code with the below code, you can quickly figure out which one is easy to understand.

 

( image source: towardsdatascience)

3)  Use functions to simplify code 

Function simplifies the code usage.  If ‘function” is declared wisely, then programmers don’t have to read each line or search the internet for things that they don’t understand.  Also, to repeat the same transformation on any column, a single line of code would do the job instead of writing lines and lines of code. 

4)  Take code out of Jupyter notebooks

Jupyter notebooks are great for quick prototyping. This is where developer — glue code, , glorified print statements (df.describe() or df.plot()), print statements, unused import statements and even stack traces. But it can make things more complicated as everything gets accumulated in one place. So, whenever a single line of code is changed, the entire notebook has to re-run. It means the longer the notebooks become, the harder it is to get feedback on whether our changes are working. The alternate way is to extract code into functions and Python modules, and have unit tests. With unit tests, the test runner can give feedback on the changes in a matter of seconds, even when there are hundreds of functions.

5)  Adopt a test-driven development approach

Unlike traditional software development, in test-driven development, you write a test for your code first, make sure it fails, and then create enough code to pass the test.  It means you are only focussed on passing the actual test and not writing code that is intended to pass all possible tests in the future. Once the developer has the code that passes the test, they can clean up the code, remove any duplication, apply good coding style rules, and add documentation. The TDD approach yields short cycles of fail-pass iterations. In the above points, we have mentioned the importance of functions, and the TDD cycle enables the programmer to write functions in small modules.

6)  Make small and frequent commits

The whole development process can greatly benefit from small and frequent commits. Often large changes in code can be difficult to review and takes a significant amount of energy. With small commits, the reviewer can detect early whenever a developer is going in the wrong direction. It also helps to reduce visual distractions and cognitive load.

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

Depending on the size of an enterprise the Learning Management System specification needs vary. Corporates looking to train a thousand or more users are scouring for LMS that can accord features for a large user base along with other imperative functionalities. In my research, below mentioned four LMS are found well-equipped to support a large number of users:1. Talent LMS:Talent LMS imparts a user-friendly, engaging and cloud-based corporate learning system ideally for large businesses. What sets it apart is its enterprise-ready interface that sanctions multiple user access, mass action execution, profiling and many other enterprise-specific features. With this platform, courses can be password protected and even watermark can be fabricated on the content. Along with security, it is also compatible with multiple operating systems such as Android or iOS. Overview of unique features:Multi – organizational structureAction automationDesignated user rolesCertificationDisplay of TranscriptsPricing:For 1000 users Talent LMS offer premium plans:$429/ month (annual billing) $529/month (monthly billing)2. SAP Litmos:  SAP Litmos is said to be the world’s simplest and easiest corporate LMS. It annihilates the cost of installation and offers training for internal as well as external teams.Overview of unique features:Platform independent tools for content creationSupport for multiple languagesSCORM and TIN CAN certifiedReal-time reportingAPIs: Salesforce, Okta & DropboxPricing: SAP Litmos offers 2 packages to support a large user basePro: $4 per user for 501 to 1000 users and custom for 1001 to 1M usersPro+ courses: $6 per user for 501 to 100 users and custom for 1001 to 1M users3. Coassemble: The perceptible quality of Coassemble is that it holds out a single platform to any enterprise irrelevant to the size of its user base. Their motto is to aid knowledge sharing and remit ease of creating content. Their ready to use drag and drop templates facilitates faster course creation. Overview of unique features:Zapler integrationCustomization of templatesTracing complianceDesign a training planExtensive storagePricing:Coassemble offers 1 package to support unlimited users$249/month (annual billing)$299/month (monthly billing) 4. eFront: eFront proffers a cosmic range of evident features but it is predominantly known for its built-in security, support for employees, customers as well as partners. It primarily gives an adaptable and scalable eLearning environment to the learners. Overview of unique features:Discussion forumsOffline learningSegmenting of usersCalendar integrationExternal pluginsPricing:eFront offers the following packages:Business plan: $1000/month for up to 1000 usersEnterprise plan: $1500/month for up to 2500 usersLarge Enterprise plan: $2500/month for up to 5000 usersTailored plan: 5000+ usersThe above research would help determine the right LMS for your corporate. 
Depending on the size of an enterprise the Learning Management System specification needs vary. Corporates looking to train a thousand or more users are scouring for LMS that can accord features for a large user base along with other imperative functionalities. In my research, below mentioned four LMS are found well-equipped to support a large number of users:1. Talent LMS:Talent LMS imparts a user-friendly, engaging and cloud-based corporate learning system ideally for large businesses. What sets it apart is its enterprise-ready interface that sanctions multiple user access, mass action execution, profiling and many other enterprise-specific features. With this platform, courses can be password protected and even watermark can be fabricated on the content. Along with security, it is also compatible with multiple operating systems such as Android or iOS. Overview of unique features:Multi – organizational structureAction automationDesignated user rolesCertificationDisplay of TranscriptsPricing:For 1000 users Talent LMS offer premium plans:$429/ month (annual billing) $529/month (monthly billing)2. SAP Litmos:  SAP Litmos is said to be the world’s simplest and easiest corporate LMS. It annihilates the cost of installation and offers training for internal as well as external teams.Overview of unique features:Platform independent tools for content creationSupport for multiple languagesSCORM and TIN CAN certifiedReal-time reportingAPIs: Salesforce, Okta & DropboxPricing: SAP Litmos offers 2 packages to support a large user basePro: $4 per user for 501 to 1000 users and custom for 1001 to 1M usersPro+ courses: $6 per user for 501 to 100 users and custom for 1001 to 1M users3. Coassemble: The perceptible quality of Coassemble is that it holds out a single platform to any enterprise irrelevant to the size of its user base. Their motto is to aid knowledge sharing and remit ease of creating content. Their ready to use drag and drop templates facilitates faster course creation. Overview of unique features:Zapler integrationCustomization of templatesTracing complianceDesign a training planExtensive storagePricing:Coassemble offers 1 package to support unlimited users$249/month (annual billing)$299/month (monthly billing) 4. eFront: eFront proffers a cosmic range of evident features but it is predominantly known for its built-in security, support for employees, customers as well as partners. It primarily gives an adaptable and scalable eLearning environment to the learners. Overview of unique features:Discussion forumsOffline learningSegmenting of usersCalendar integrationExternal pluginsPricing:eFront offers the following packages:Business plan: $1000/month for up to 1000 usersEnterprise plan: $1500/month for up to 2500 usersLarge Enterprise plan: $2500/month for up to 5000 usersTailored plan: 5000+ usersThe above research would help determine the right LMS for your corporate. 

Depending on the size of an enterprise the Learning Management System specification needs vary. Corporates looking to train a thousand or more users are scouring for LMS that can accord features for a large user base along with other imperative functionalities. 

In my research, below mentioned four LMS are found well-equipped to support a large number of users:

1. Talent LMS:

Talent LMS imparts a user-friendly, engaging and cloud-based corporate learning system ideally for large businesses. What sets it apart is its enterprise-ready interface that sanctions multiple user access, mass action execution, profiling and many other enterprise-specific features. 

With this platform, courses can be password protected and even watermark can be fabricated on the content. Along with security, it is also compatible with multiple operating systems such as Android or iOS. 

Overview of unique features:

  1. Multi – organizational structure
  2. Action automation
  3. Designated user roles
  4. Certification
  5. Display of Transcripts

Pricing:

For 1000 users Talent LMS offer premium plans:

  1. $429/ month (annual billing) 
  2. $529/month (monthly billing)

2. SAP Litmos:  

SAP Litmos is said to be the world’s simplest and easiest corporate LMS. It annihilates the cost of installation and offers training for internal as well as external teams.

Overview of unique features:

  1. Platform independent tools for content creation
  2. Support for multiple languages
  3. SCORM and TIN CAN certified
  4. Real-time reporting
  5. APIs: Salesforce, Okta & Dropbox

Pricing: 

SAP Litmos offers 2 packages to support a large user base

  1. Pro: $4 per user for 501 to 1000 users and custom for 1001 to 1M users
  2. Pro+ courses: $6 per user for 501 to 100 users and custom for 1001 to 1M users

3. Coassemble: 

The perceptible quality of Coassemble is that it holds out a single platform to any enterprise irrelevant to the size of its user base. Their motto is to aid knowledge sharing and remit ease of creating content. Their ready to use drag and drop templates facilitates faster course creation. 

Overview of unique features:

  1. Zapler integration
  2. Customization of templates
  3. Tracing compliance
  4. Design a training plan
  5. Extensive storage

Pricing:

Coassemble offers 1 package to support unlimited users

  1. $249/month (annual billing)
  2. $299/month (monthly billing) 

4. eFront: 

eFront proffers a cosmic range of evident features but it is predominantly known for its built-in security, support for employees, customers as well as partners. It primarily gives an adaptable and scalable eLearning environment to the learners. 

Overview of unique features:

  1. Discussion forums
  2. Offline learning
  3. Segmenting of users
  4. Calendar integration
  5. External plugins

Pricing:

eFront offers the following packages:

  1. Business plan: $1000/month for up to 1000 users
  2. Enterprise plan: $1500/month for up to 2500 users
  3. Large Enterprise plan: $2500/month for up to 5000 users
  4. Tailored plan: 5000+ users

The above research would help determine the right LMS for your corporate. 

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 has disrupted the education and traditional systems worldwide – 1.2 billion+ children are now out of the classroom. As soon as the education entities learned the nature of the COVID-19 virus, they quickly shifted to the online methods of learning, teaching and grading. And with the increasing cases and second waves of the Coronavirus everywhere, online education is the only saviour students have - till we find a promising vaccine. In my opinion, e-learning will not entirely replace classroom education. But, the hybrid solution – a mix of online and offline will be embraced. Many predicted that online learning is the future of higher education – but nothing is certain, there’s always a grey area that we should not avoid. One size does not fit all - the repercussions of these sudden changes have impacted students’ thought process and their ability to understand a subject. Therefore, the return of in-campus/in-school teaching is destined (we just don’t know when but it will). If you say that e-learning has precedence over classroom education – then you are being ignorant. There are a lot of students who are not in favour of online education and miss in-campus and school life. It is not about textbook learning and lectures but an immersive and holistic experience. It teaches you valuable life skills – adaptability, sociability and living outside your comfort zone. Secondly, online learning is still a “privileged concept” for numerous families. In a country like India – many families don’t even have access to robust “internet connection” let alone gadgets. For effective online learning – a student needs a decent laptop/tablet, high-speed internet and a room without distractions. Schools and colleges are the great levellers – they provide the same ecosystem and services to everyone.  Thirdly, online learning ensures less supervision meaning you cannot fully understand which student is struggling with a topic. It’s tough for teachers to clear the doubts and make sure everyone is on the same page. Due to this reason, students don’t perform well in examinations and tests. Lastly, the “screen time” of students has dramatically magnified – with the majority of students spending 5-6 hours in online classes. This can lead to severe headaches, weak eyesight and can cause concentration difficulties. Moreover, this at-home learning is also giving a significant surge to sedentary lifestyles in students. Final words Be it working, shopping, entertainment and education - the pandemic has become an inflection point for new inventions. But what we lack is a more human-like approach in these innovations. And that is why the traditional way of teaching and learning will not go out of style, not soon I believe. 
The pandemic has disrupted the education and traditional systems worldwide – 1.2 billion+ children are now out of the classroom. As soon as the education entities learned the nature of the COVID-19 virus, they quickly shifted to the online methods of learning, teaching and grading. And with the increasing cases and second waves of the Coronavirus everywhere, online education is the only saviour students have - till we find a promising vaccine. In my opinion, e-learning will not entirely replace classroom education. But, the hybrid solution – a mix of online and offline will be embraced. Many predicted that online learning is the future of higher education – but nothing is certain, there’s always a grey area that we should not avoid. One size does not fit all - the repercussions of these sudden changes have impacted students’ thought process and their ability to understand a subject. Therefore, the return of in-campus/in-school teaching is destined (we just don’t know when but it will). If you say that e-learning has precedence over classroom education – then you are being ignorant. There are a lot of students who are not in favour of online education and miss in-campus and school life. It is not about textbook learning and lectures but an immersive and holistic experience. It teaches you valuable life skills – adaptability, sociability and living outside your comfort zone. Secondly, online learning is still a “privileged concept” for numerous families. In a country like India – many families don’t even have access to robust “internet connection” let alone gadgets. For effective online learning – a student needs a decent laptop/tablet, high-speed internet and a room without distractions. Schools and colleges are the great levellers – they provide the same ecosystem and services to everyone.  Thirdly, online learning ensures less supervision meaning you cannot fully understand which student is struggling with a topic. It’s tough for teachers to clear the doubts and make sure everyone is on the same page. Due to this reason, students don’t perform well in examinations and tests. Lastly, the “screen time” of students has dramatically magnified – with the majority of students spending 5-6 hours in online classes. This can lead to severe headaches, weak eyesight and can cause concentration difficulties. Moreover, this at-home learning is also giving a significant surge to sedentary lifestyles in students. Final words Be it working, shopping, entertainment and education - the pandemic has become an inflection point for new inventions. But what we lack is a more human-like approach in these innovations. And that is why the traditional way of teaching and learning will not go out of style, not soon I believe. 

The pandemic has disrupted the education and traditional systems worldwide – 1.2 billion+ children are now out of the classroom. As soon as the education entities learned the nature of the COVID-19 virus, they quickly shifted to the online methods of learning, teaching and grading. And with the increasing cases and second waves of the Coronavirus everywhere, online education is the only saviour students have - till we find a promising vaccine. 

In my opinion, e-learning will not entirely replace classroom education. But, the hybrid solution – a mix of online and offline will be embraced. Many predicted that online learning is the future of higher education – but nothing is certain, there’s always a grey area that we should not avoid. 

One size does not fit all - the repercussions of these sudden changes have impacted students’ thought process and their ability to understand a subject. Therefore, the return of in-campus/in-school teaching is destined (we just don’t know when but it will). 

If you say that e-learning has precedence over classroom education – then you are being ignorant. There are a lot of students who are not in favour of online education and miss in-campus and school life. It is not about textbook learning and lectures but an immersive and holistic experience. It teaches you valuable life skills – adaptability, sociability and living outside your comfort zone. 

Secondly, online learning is still a “privileged concept” for numerous families. In a country like India – many families don’t even have access to robust “internet connection” let alone gadgets. For effective online learning – a student needs a decent laptop/tablet, high-speed internet and a room without distractions. Schools and colleges are the great levellers – they provide the same ecosystem and services to everyone.  

Thirdly, online learning ensures less supervision meaning you cannot fully understand which student is struggling with a topic. It’s tough for teachers to clear the doubts and make sure everyone is on the same page. Due to this reason, students don’t perform well in examinations and tests. 

Lastly, the “screen time” of students has dramatically magnified – with the majority of students spending 5-6 hours in online classes. This can lead to severe headaches, weak eyesight and can cause concentration difficulties. Moreover, this at-home learning is also giving a significant surge to sedentary lifestyles in students. 

Final words 

Be it working, shopping, entertainment and education - the pandemic has become an inflection point for new inventions. But what we lack is a more human-like approach in these innovations. And that is why the traditional way of teaching and learning will not go out of style, not soon I believe. 

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