Thoughtful Machine Learning with Python Strumień

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.Featuring graphs and highlighted code examples throughout, the book features tests with Python...s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you...re a software engineer or business analyst interested in data science, this book will help you:Reference real-world examples to test each algorithm through engaging, hands-on exercisesApply test-driven development (TDD) to write and run tests before you start codingExplore techniques for improving your machine-learning models with data extraction and feature developmentWatch out for the risks of machine learning, such as underfitting or overfitting dataWork with K-Nearest Neighbors, neural networks, clustering, and other algorithms Spis treści: Preface Conventions Used in This Book Using Code Examples OReilly Safari How to Contact Us Acknowledgments 1. Probably Approximately Correct Software Writing Software Right SOLID Single Responsibility Principle Open/Closed Principle Liskov Substitution Principle Interface Segregation Principle Dependency Inversion Principle Testing or TDD Refactoring Writing the Right Software Writing the Right Software with Machine Learning What Exactly Is Machine Learning? The High Interest Credit Card Debt of Machine Learning SOLID Applied to Machine Learning SRP OCP LSP ISP DIP Machine Learning Code Is Complex but Not Impossible TDD: Scientific Method 2.0 Refactoring Our Way to Knowledge 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. Ks Nearest Neighborhood Distances Triangle Inequality Geometrical Distance Cosine similarity Computational Distances Manhattan distance Levenshtein distance Statistical Distances Mahalanobis distance Jaccard distance Curse of Dimensionality How Do We Pick K? Guessing K Heuristics for Picking K Use coprime class and K combinations Choose a K that is greater or equal to the number of classes plus one Choose a K that is low enough to avoid noise Algorithms for picking K Valuing Houses in Seattle About the Data General Strategy Coding and Testing Design KNN Regressor Construction KNN Testing 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 The Chain Rule Naiveté in Bayesian Reasoning Pseudocount Spam Filter Setup Notes Coding and Testing Design Data Source Email Class Tokenization and Context SpamTrainer Storing training data Building the Bayesian classifier Calculating a classification Error Minimization Through Cross-Validation Minimizing false positives Building the two folds Cross-validation and error measuring Conclusion 5. Decision Trees and Random Forests The Nuances of Mushrooms Classifying Mushrooms Using a Folk Theorem Finding an Optimal Switch Point Information Gain GINI Impurity Variance Reduction Pruning Trees Ensemble Learning Bagging Random forests Writing a Mushroom Classifier Coding and testing design MushroomProblem Testing Conclusion 6. Hidden Markov Models Tracking User Behavior Using State Machines Emissions/Observations of Underlying States Simplification Through the Markov Assumption Using Markov Chains Instead of a Finite State Machine Hidden Markov Model Evaluation: Forward-Backward Algorithm Mathematical Representation of the Forward-Backward Algorithm Using User Behavior The Decoding Problem Through the Viterbi Algorithm The Learning Problem Part-of-Speech Tagging with the Brown Corpus Setup Notes Coding and Testing Design The Seam of Our Part-of-Speech Tagger: CorpusParser Writing the Part-of-Speech Tagger Cross-Validating to Get Confidence in the Model How to Make This Model Better Conclusion 7. Support Vector Machines Customer Happiness as a Function of What They Say Sentiment Classification Using SVMs The Theory Behind SVMs Decision Boundary Maximizing Boundaries Kernel Trick: Feature Transformation Optimizing with Slack Sentiment Analyzer Setup Notes Coding and Testing Design SVM Testing Strategies Corpus Class CorpusSet Class Model Validation and the Sentiment Classifier Aggregating Sentiment Exponentially Weighted Moving Average 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 Input Layer Standard inputs Symmetric inputs Hidden Layers Neurons Activation Functions Output Layer Training Algorithms The Delta Rule Back Propagation QuickProp RProp Building Neural Networks How Many Hidden Layers? How Many Neurons for Each Layer? Tolerance for Error and Max Epochs Using a Neural Network to Classify a Language Setup Notes Coding and Testing Design The Data Writing the Seam Test for Language Cross-Validating Our Way to a Network Class Tuning the Neural Network Precision and Recall for Neural Networks Wrap-Up of Example Conclusion 9. Clustering Studying Data Without Any Bias User Cohorts Testing Cluster Mappings Fitness of a Cluster Silhouette Coefficient Comparing Results to Ground Truth K-Means Clustering The K-Means Algorithm Downside of K-Means Clustering EM Clustering Algorithm Expectation Maximization The Impossibility Theorem Example: Categorizing Music Setup Notes Gathering the Data Coding Design Analyzing the Data with K-Means EM Clustering Our Data The Results from the EM Jazz Clustering Conclusion 10. Improving Models and Data Extraction Debate Club Picking Better Data Feature Selection Exhaustive Search Random Feature Selection A Better Feature Selection Algorithm Minimum Redundancy Maximum Relevance Feature Selection Feature Transformation and Matrix Factorization Principal Component Analysis Independent Component Analysis Ensemble Learning Bagging Boosting Conclusion 11. Putting It Together: Conclusion Machine Learning Algorithms Revisited How to Use This Information to Solve Problems Whats Next for You? Index