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Introduction to Machine learning

                             

                               MACHINE LEARNING 


Machine learning gives computers the ability to learn without being explicitly programmed. it is the ability of
computer programs to analyze data, extract information automatically, and learn from it.
machine learning is closely related to (and often
overlaps with) computer statistics. it has strong ties of mathematical optimization. machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics.

So, basically, implementing machine learning means creating algorithms which can learn and make predictions on data. 
example of  machine learning:
-Google's search engine, that ranks the websites by relevancy.
-Self-driving cars. 
-Email spam filters.

All of the systems above learn and improve themselves as more and more data is provided: The more emails you mark as spam, the better the system filters new emails. The more situations the self-driving car navigates, the better it drives. 
                                                                  This is very similar to how we humans learn. A typical example is a problem of classification. For instance, the emails spam filter needs to classify the spam and not spam emails. Note, that this is quite user-dependent. if we know that a specific user is marking all the travel emails as spam, we are going to classify the new emails more effectively. More relevant data leads to better predictions.

This is only the introduction of machine learning. if you want more to learn related to this topic. so search more in programming one on one.

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