contents

preface
acknowledgments
about this book
about the author
about the cover illustration

Part 1 Classification

1 Machine learning basics: 1.1 What is machine learning?; 1.2 Key terminology; 1.3 Key tasks of machine learning; 1.4 How to choose the right algorithm; 1.5 Steps in developing a machine learning application; 1.6 Why Python?; 1.7 Getting started with the NumPy library; 1.8 Summary
2 Classifying with k-Nearest Neighbors: 2.1 Classifying with distance measurements; 2.2 Example: improving matches from a dating site with kNN; 2.3 Example: a handwriting recognition system; 2.4 Summary
3 Splitting datasets one feature at a time: decision trees: 3.1 Tree construction; 3.2 Plotting trees in Python with Matplotlib annotations; 3.3 Testing and storing the classifier; 3.4 Example: using decision trees to predict contact lens type; 3.5 Summary
4 Classifying with probability theory: naïve Bayes: 4.1 Classifying with Bayesian decision theory; 4.2 Conditional probability; 4.3 Classifying with conditional probabilities; 4.4 Document classification with naïve Bayes; 4.5 Classifying text with Python; 4.6 Example: classifying spam email with naïve Bayes; 4.7 Example: using naïve Bayes to reveal local attitudes from personal ads; 4.8 Summary
5 Logistic regression: 5.1 Classification with logistic regression and the sigmoid function: a tractable step function; 5.2 Using optimization to find the best regression coefficients; 5.3 Example: estimating horse fatalities from colic; 5.4 Summary
6 Support vector machines: 6.1 Separating data with the maximum margin; 6.2 Finding the maximum margin; 6.3 Efficient optimization with the SMO algorithm; 6.4 Speeding up optimization with the full Platt SMO; 6.5 Using kernels for more complex data; 6.6 Example: revisiting handwriting classification; 6.7 Summary
7 Improving classification with the AdaBoost meta-algorithm: 7.1 Classifiers using multiple samples of the dataset; 7.2 Train: improving the classifier by focusing on errors; 7.3 Creating a weak learner with a decision stump; 7.4 Implementing the full AdaBoost algorithm; 7.5 Test: classifying with AdaBoost; 7.6 Example: AdaBoost on a difficult dataset; 7.7 Classification imbalance; 7.8 Summary

Part 2 Forecasting numeric values with regression

8 Predicting numeric values: regression: 8.1 Finding best-fit lines with linear regression; 8.2 Locally weighted linear regression; 8.3 Example: predicting the age of an abalone; 8.4 Shrinking coefficients to understand our data; 8.5 The bias/variance tradeoff; 8.6 Example: forecasting the price of LEGO sets; 8.7 Summary
9 Tree-based regression: 9.1 Locally modeling complex data; 9.2 Building trees with continuous and discrete features; 9.3 Using CART for regression; 9.4 Tree pruning; 9.5 Model trees; 9.6 Example: comparing tree methods to standard regression; 9.7 Using Tkinter to create a GUI in Python; 9.8 Summary

Part 3 Unsupervised learning

10 Grouping unlabeled items using k-means clustering: 10.1 The k-means clustering algorithm; 10.2 Improving cluster performance with postprocessing; 10.3 Bisecting k-means; 10.4 Example: clustering points on a map; 10.5 Summary
11 Association analysis with the Apriori algorithm: 11.1 Association analysis; 11.2 The Apriori principle; 11.3 Finding frequent itemsets with the Apriori algorithm; 11.4 Mining association rules from frequent item sets; 11.5 Example: uncovering patterns in congressional voting; 11.6 Example: finding similar features in poisonous mushrooms; 11.7 Summary
12 Efficiently finding frequent itemsets with FP-growth: 12.1 FP-trees: an efficient way to encode a dataset; 12.2 Build an FP-tree; 12.3 Mining frequent items from an FP-tree; 12.4 Example: finding co-occurring words in a Twitter feed; 12.5 Example: mining a clickstream from a news site; 12.6 Summary

Part 4 Additional tools

13 Using principal component analysis to simplify data: 13.1 Dimensionality reduction techniques; 13.2 Principal component analysis; 13.3 Example: using PCA to reduce the dimensionality of semiconductor manufacturing data; 13.4 Summary
14 Simplifying data with the singular value decomposition: 14 .1 Applications of the SVD; 14.2 Matrix factorization; 14.3 SVD in Python; 14.4 Collaborative filtering–based recommendation engines; 14.5 Example: a restaurant dish recommendation engine; 14.6 Example: image compression with the SVD; 14.7 Summary
15 Big data and MapReduce: 15.1 MapReduce: a framework for distributed computing; 15.2 Hadoop Streaming; 15.3 Running Hadoop jobs on Amazon Web Services; 15.4 Machine learning in MapReduce; 15.5 Using mrjob to automate MapReduce in Python; 15.6 Example: the Pegasos algorithm for distributed SVMs; 15.7 Do you really need MapReduce?; 15.8 Summary