Introduction to Machine Learning

This blog is purely for Study purpose, it is created by taking notes from Andrew Ng's Coursera Course.

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Supervised Learning

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Supervised Learning: We provide a dataset of right answers and ask the Machine to get trained on those datasets, later on once the Machine builds a Model out of those datasets, we ask the Machine to predict the outcome when provided with similar data. In simple terms, the Machine first learns from the given data and then predicts the outcomes based on that. 1) Housing Price Prediction: Andrew Ng used Housing Price Prediction problem for the same which is considered as a Regression Problem. The dataset contains 2 columns, Size in sq. ft. and Price in $1000. Now when the Machine gets trained with this dataset and we can ask the Machine about what would be the Price of the House given the Area of the house. In here also Size in sq. ft. is the ONLY Feature/Attribute. Pink Line denotes a Linear equation Blue Line denotes a Qudratic equation with polynomial 2 Let's not worry about how to differentiate about which one to choose now. 2) Breast Cancer Prediction: In t...
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What is Machine Learning ?

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Old Machine Learning Definition: Field of study that gives computers the ability to learn without being explicitly programmed. By Arthur Samuel (1959) The above statement by Arthur Samuel is considered an Old one. A new one was stated by Tom Mitchell, a friend of Carnegie Melon. New Machine Learning Definition:  A computer program is said to learn from experience E with respect to some task T and some performance measure P , if its performance on T, as measured by P, improves with experience E. By Tom Mitchell (1998) Experience E: I think this is the data collected overtime. Task T: Action from the experience Performance P: Probability of right action A Question asked by Andre Ng in his class. Types of Learning: 1) Supervised Learning:  We teach the computer how to do something 2) Unsupervised Learning: The computer learns by itself.
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