What this book covers
Chapter 1, Introduction - Machine Learning and Statistical Science, covers various introductory concepts in machine learning. It talks about the history, branches and general discipline concepts. It also gives an introduction to the base mathematical concepts needed to understand most of the techniques developed afterward.
Chapter 2, The Learning Process, covers all the steps in the workflow of a machine learning process and shows useful tools and concept definitions for all those stages.
Chapter 3, Clustering, covers several techniques for unsupervised learning, specially K-Means, and K-NN clustering.
Chapter 4, Linear and Logistic Regression, covers two pretty different supervised learning algorithms, which go under ...
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