Thoughtful Machine Learning with Python

Preface

1. Probably Approximately Correct Software
-Writing Software Right
-Writing the Right Software
-The Plan for the Book

2. A Quick Introduction to Machine Learning
-What Is Machine Learning?
-Supervised Learning
-Unsupervised Learning
-Reinforcement Learning
-What Can Machine Learning Accomplish?
-Mathematical Notation Used Throughout the Book
-Conclusion

3. K-Nearest Neighbors
-How Do You Determine Whether You Want to Buy a House?
-How Valuable Is That House?
-Hedonic Regression
-What Is a Neighborhood?
-K-Nearest Neighbors
-Mr. K’s Nearest Neighborhood
-Distances
-Curse of Dimensionality
-How Do We Pick K?
-Valuing Houses in Seattle
-Conclusion

4. Naive Bayesian Classification
-Using Bayes’ Theorem to Find Fraudulent Orders
-Conditional Probabilities
-Probability Symbols
-Inverse Conditional Probability (aka Bayes’ Theorem)
-Naive Bayesian Classifier
-Naiveté in Bayesian Reasoning
-Pseudocount
-Spam Filter
-Conclusion

5. Decision Trees and Random Forests
-The Nuances of Mushrooms
-Classifying Mushrooms Using a Folk Theorem
-Finding an Optimal Switch Point
-Pruning Trees
-Conclusion

6. Hidden Markov Models
-Tracking User Behavior Using State Machines
-Emissions/Observations of Underlying States
-Simplification Through the Markov Assumption
-Hidden Markov Model
-Evaluation: Forward-Backward Algorithm
-The Decoding Problem Through the Viterbi Algorithm
-The Learning Problem
-Part-of-Speech Tagging with the Brown Corpus
-Conclusion

7. Support Vector Machines
-Customer Happiness as a Function of What They Say
-The Theory Behind SVMs
-Sentiment Analyzer
-Aggregating Sentiment
-Mapping Sentiment to Bottom Line
-Conclusion

8. Neural Networks
-What Is a Neural Network?
-History of Neural Nets
-Boolean Logic
-Perceptrons
-How to Construct Feed-Forward Neural Nets
-Building Neural Networks
-Using a Neural Network to Classify a Language

9. Clustering
-Studying Data Without Any Bias
-User Cohorts
-Testing Cluster Mappings
-K-Means Clustering
-EM Clustering
-The Impossibility Theorem
-Example: Categorizing Music
-Conclusion

10. Improving Models and Data Extraction
-Debate Club
-Picking Better Data
-Feature Transformation and Matrix Factorization
-Ensemble Learning
-Conclusion

11. Putting It Together: Conclusion
-Machine Learning Algorithms Revisited
-How to Use This Information to Solve Problems
-What’s Next for You?

Index