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Machine Learning Tutorial

Machine Learning is a term that is used often and is misinterpreted many a times. Certain movies create an unrealistic expectation by showing extra-ordinary things which could be achieved with the help of Machine Learning. It is important to understand what Machine Learning can help us achieve and what not. It is important to understand that Machine Learning is a branch of Artificial Intelligence that helps in achieving certain goals. It can be understood as a process where data is provided to the machine and no explicit rules are defined for the machine to learn from. It just learns from the data it has been provided. It doesn’t consist of hardcoded ‘if’ and ‘else’ statements in it. The Machine Learning algorithms extract patterns from data and learn from them, like how humans learn based on experiences. Machine Learning algorithms can be classified into different types of learning based on the input and the type of input that is supplied. They can also be classified based on what they output, how they learn and what modules can be used to implement them. Machine Learning is widely used for applications such as data mining, computer vision, Natural Language Processing, Search engines, credit card fraud detection, speech and handwriting recognition, strategy games, robotics, and much more. 

Machine Learning Tutorial

Covers 29 chapters on Machine Learning

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Machine Learning Tutorial

Machine Learning is a term that is used often and is misinterpreted many a times. Certain movies create an unrealistic expectation by showing extra-ordinary things which could be achieved with the help of Machine Learning. It is important to understand what Machine Learning can help us achieve and what not. It is important to understand that Machine Learning is a branch of Artificial Intelligence that helps in achieving certain goals. 

It can be understood as a process where data is provided to the machine and no explicit rules are defined for the machine to learn from. It just learns from the data it has been provided. It doesn’t consist of hardcoded ‘if’ and ‘else’ statements in it. 

The Machine Learning algorithms extract patterns from data and learn from them, like how humans learn based on experiences. 

Machine Learning algorithms can be classified into different types of learning based on the input and the type of input that is supplied. They can also be classified based on what they output, how they learn and what modules can be used to implement them. 

Machine Learning is widely used for applications such as data mining, computer vision, Natural Language Processing, Search engines, credit card fraud detection, speech and handwriting recognition, strategy games, robotics, and much more. 

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