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Chapter 1: Introduction to Machine Learning
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Chapter 2: Making Decisions with Trees
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Chapter 3: Making Decisions with LinearEquations
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Chapter 4: Preparing Your Data
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Chapter 5: Image Processing with NearestNeighbors
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Chapter 6: Classifying Text Using NaiveBayes
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Chapter 7: Neural Networks - Here ComesDeep Learning
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Chapter 8: Ensembles - When One Model IsNot Enough
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Chapter 9: The Y is as Important as the X
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Chapter 10: Imbalanced Learning - Not Even1% Win the Lottery
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Chapter 11: Clustering - Making Sense ofUnlabeled Data
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Chapter 12: Anomaly Detection - FindingOutliers in Data
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Chapter 13: Recommender System - Gettingto Know Their Taste
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