Chapter 1: Introduction to Machine Learning
[ 2 ]
[ 3 ]
Chapter 2: Making Decisions with Trees
[ 4 ]
[ 5 ]
[ 6 ]
[ 7 ]
Chapter 3: Making Decisions with LinearEquations
[ 8 ]
[ 9 ]
[ 10 ]
[ 11 ]
[ 12 ]
[ 13 ]
[ 14 ]
[ 15 ]
Chapter 4: Preparing Your Data
[ 16 ]
[ 17 ]
[ 18 ]
[ 19 ]
Chapter 5: Image Processing with NearestNeighbors
[ 20 ]
[ 21 ]
[ 22 ]
[ 23 ]
[ 24 ]
[ 25 ]
[ 26 ]
Chapter 6: Classifying Text Using NaiveBayes
[ 27 ]
[ 28 ]
[ 29 ]
Chapter 7: Neural Networks - Here ComesDeep Learning
[ 30 ]
[ 31 ]
[ 32 ]
[ 33 ]
[ 34 ]
[ 35 ]
[ 36 ]
Chapter 8: Ensembles - When One Model IsNot Enough
[ 37 ]
[ 38 ]
[ 39 ]
[ 40 ]
[ 41 ]
[ 42 ]
[ 43 ]
[ 44 ]
[ 45 ]
Chapter 9: The Y is as Important as the X
[ 46 ]
[ 47 ]
[ 48 ]
[ 49 ]
[ 50 ]
Chapter 10: Imbalanced Learning - Not Even1% Win the Lottery
[ 51 ]
[ 52 ]
[ 53 ]
[ 54 ]
Chapter 11: Clustering - Making Sense ofUnlabeled Data
[ 55 ]
[ 56 ]
[ 57 ]
[ 58 ]
[ 59 ]
Chapter 12: Anomaly Detection - FindingOutliers in Data
[ 60 ]
[ 61 ]
Chapter 13: Recommender System - Gettingto Know Their Taste
[ 62 ]