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Machine Learning Tutorial

We all use the word Machine Learning to imply that machines are learning by themselves with the help of data which was supplied to it. But let us dig a little deeper into it and understand what it actually is, and why it has to be used (not everywhere but only when certain vital conditions are fulfilled). In simple words, machine learning can be considered as the art of teaching machines to learn from the data it has been supplied with (by humans).It is equally important to know the hierarchy of machine learning. It is a sub-class of Artificial Intelligence (AI) which doesn't need a human to explicitly write rules from which the machines would learn. The humans are responsible in providing data with the help of which the machine would learn. The below image shows the hierarchy of machine learning:The word Machine learning has been hyped up due to which people assume that it can be used to replace humans. But that is not true. Machines are made to help humans do tasks in a better way, by taking much less time, thereby saving resources.  Visualize it this way  Humans needs to learn the process of addition and then add numbers. If the numbers are very huge, the human would take more time. On the other hand, computers are just programmed how addition works. Irrespective of whether the number is huge or not, they give the result in fraction of seconds.  Apply the same idea to a slightly more complicated task  Suppose a person keeps getting 100 spam emails every day and just 20 important emails. What is the general intuition? The person goes through the subject of the email, sometimes the subject could be misleading too. Assume the worst-case scenario (which means the subject line is misleading), hence the  person has to open every email and scan through the contents to know if it is an important mail or just an advertisement/promotion/spam. Now the same person decides to write a program that would detect spam emails for them. He scans through the 80 spam emails, finds out words, sentences, number of words, the way in which these words have been phrased, etc and writes rules which state that when such a condition is encountered, the email has to be classified as a spam email. This works just fine. Now assume a spam email comes which is completely different and doesn’t follow the rules that the person wrote. The spam email would be classified as an important email. The person would again need to rewrite the rules or add in another set of rules to identify such spam emails. How long will this go on? This is where Machine Learning algorithms come into play. To be specific, classification algorithms which help the user in classifying whether an email that they received is spam or important. Your next question would be- What is the input data to such a classification algorithm? It is the user feedback about emails. When an email comes in, the user has the option to categorize it as spam or important. If the user does it a couple more times, the classifier learns to categorize spam emails. In best cases, about 99 percent of the emails are classified properly. This way, the user wouldn’t need to scan through every email to check its importance. But when the classifier can’t categorize an email as spam or important, it just puts the email in ‘important’ folder so that the user doesn’t miss out on anything important. The above explained example is a simple use case of machine learning, which is where it all started. When should Machine Learning be used instead of writing logic for programs to perform tasks? When there is a requirement to write thousands to millions of rules for a specific task: Instead of writing/designing the logic, a machine learning algorithm can be implemented which helps in avoiding writing the rules. When new data comes in very often and the rules have to be rewritten, changed or added: When new data pops in frequently, instead of changing rules, relevant machine learning algorithms can be used since they have the ability to adapt to new data. When the amount of data is huge and useful insights need to be extracted from them: Traditional approach wouldn't work, and this is when machine learning tools can be used to visualize large amounts of data, predict values of the near future and help in taking important decisions for businesses. Conclusion In this post, we understood the exact meaning of Machine Learning and also saw a use case of machine learning algorithm in action. 
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Machine Learning Tutorial

Demystifying Machine Learning

We all use the word Machine Learning to imply that machines are learning by themselves with the help of data which was supplied to it. But let us dig a little deeper into it and understand what it actually is, and why it has to be used (not everywhere but only when certain vital conditions are fulfilled). In simple words, machine learning can be considered as the art of teaching machines to learn from the data it has been supplied with (by humans).

It is equally important to know the hierarchy of machine learning. It is a sub-class of Artificial Intelligence (AI) which doesn't need a human to explicitly write rules from which the machines would learn. The humans are responsible in providing data with the help of which the machine would learn. The below image shows the hierarchy of machine learning:

The word Machine learning has been hyped up due to which people assume that it can be used to replace humans. But that is not true. Machines are made to help humans do tasks in a better way, by taking much less time, thereby saving resources.  

Visualize it this way  

Humans needs to learn the process of addition and then add numbers. If the numbers are very huge, the human would take more time. On the other hand, computers are just programmed how addition works. Irrespective of whether the number is huge or not, they give the result in fraction of seconds.  

Apply the same idea to a slightly more complicated task  

Suppose a person keeps getting 100 spam emails every day and just 20 important emails. What is the general intuition? The person goes through the subject of the email, sometimes the subject could be misleading too. Assume the worst-case scenario (which means the subject line is misleading), hence the  

person has to open every email and scan through the contents to know if it is an important mail or just an advertisement/promotion/spam. 

Now the same person decides to write a program that would detect spam emails for them. He scans through the 80 spam emails, finds out words, sentences, number of words, the way in which these words have been phrased, etc and writes rules which state that when such a condition is encountered, the email has to be classified as a spam email. This works just fine. 

Now assume a spam email comes which is completely different and doesn’t follow the rules that the person wrote. The spam email would be classified as an important email. The person would again need to rewrite the rules or add in another set of rules to identify such spam emails. 

How long will this go on? 

This is where Machine Learning algorithms come into play. To be specific, classification algorithms which help the user in classifying whether an email that they received is spam or important. 

Your next question would be- What is the input data to such a classification algorithm? It is the user feedback about emails. When an email comes in, the user has the option to categorize it as spam or important. If the user does it a couple more times, the classifier learns to categorize spam emails. In best cases, about 99 percent of the emails are classified properly. This way, the user wouldn’t need to scan through every email to check its importance. But when the classifier can’t categorize an email as spam or important, it just puts the email in ‘important’ folder so that the user doesn’t miss out on anything important. 

The above explained example is a simple use case of machine learning, which is where it all started. 

When should Machine Learning be used instead of writing logic for programs to perform tasks? 

  • When there is a requirement to write thousands to millions of rules for a specific task: Instead of writing/designing the logic, a machine learning algorithm can be implemented which helps in avoiding writing the rules. 
  • When new data comes in very often and the rules have to be rewritten, changed or added: When new data pops in frequently, instead of changing rules, relevant machine learning algorithms can be used since they have the ability to adapt to new data. 
  • When the amount of data is huge and useful insights need to be extracted from them: Traditional approach wouldn't work, and this is when machine learning tools can be used to visualize large amounts of data, predict values of the near future and help in taking important decisions for businesses. 

Conclusion 

In this post, we understood the exact meaning of Machine Learning and also saw a use case of machine learning algorithm in action. 

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