Brief Table of Contents
Chapter 1. Machine learning basics
Chapter 2. Classifying with k-Nearest Neighbors
Chapter 3. Splitting datasets one feature at a time: decision trees
Chapter 4. Classifying with probability theory: naïve Bayes
Chapter 5. Logistic regression
Chapter 6. Support vector machines
Chapter 7. Improving classification with the AdaBoost meta-algorithm
2. Forecasting numeric values with regression
Chapter 10. Grouping unlabeled items using k-means clustering
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