Thoughtful Machine Learning with Python. A Test-Driven Approach (ebook)(audiobook)(audiobook)

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

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