Books & Videos

Table of Contents

  1. Chapter 1 Test-Driven Machine Learning

    1. History of Test-Driven Development

    2. TDD and the Scientific Method

    3. Risks with Machine Learning

    4. What to Test for to Reduce Risks

    5. Conclusion

  2. Chapter 2 A Quick Introduction to Machine Learning

    1. What Is Machine Learning?

    2. What Can Machine Learning Accomplish?

    3. Mathematical Notation Used Throughout the Book

    4. Conclusion

  3. Chapter 3 K-Nearest Neighbors Classification

    1. History of K-Nearest Neighbors Classification

    2. House Happiness Based on a Neighborhood

    3. How Do You Pick K?

    4. What Makes a Neighbor “Near”?

    5. Determining Classes

    6. Beard and Glasses Detection Using KNN and OpenCV

    7. Conclusion

  4. Chapter 4 Naive Bayesian Classification

    1. Using Bayes’s Theorem to Find Fraudulent Orders

    2. Naive Bayesian Classifier

    3. Spam Filter

    4. Conclusion

  5. Chapter 5 Hidden Markov Models

    1. Tracking User Behavior Using State Machines

    2. Evaluation: Forward-Backward Algorithm

    3. The Decoding Problem through the Viterbi Algorithm

    4. The Learning Problem

    5. Part-of-Speech Tagging with the Brown Corpus

    6. Conclusion

  6. Chapter 6 Support Vector Machines

    1. Solving the Loyalty Mapping Problem

    2. Derivation of SVM

    3. Nonlinear Data

    4. Using SVM to Determine Sentiment

    5. Conclusion

  7. Chapter 7 Neural Networks

    1. History of Neural Networks

    2. What Is an Artificial Neural Network?

    3. Building Neural Networks

    4. Using a Neural Network to Classify a Language

    5. Conclusion

  8. Chapter 8 Clustering

    1. User Cohorts

    2. K-Means Clustering

    3. Expectation Maximization (EM) Clustering

    4. The Impossibility Theorem

    5. Categorizing Music

    6. Conclusion

  9. Chapter 9 Kernel Ridge Regression

    1. Collaborative Filtering

    2. Linear Regression Applied to Collaborative Filtering

    3. Introducing Regularization, or Ridge Regression

    4. Kernel Ridge Regression

    5. Wrap-Up of Theory

    6. Collaborative Filtering with Beer Styles

    7. Conclusion

  10. Chapter 10 Improving Models and Data Extraction

    1. The Problem with the Curse of Dimensionality

    2. Feature Selection

    3. Feature Transformation

    4. Principal Component Analysis (PCA)

    5. Independent Component Analysis (ICA)

    6. Monitoring Machine Learning Algorithms

    7. Mean Squared Error

    8. The Wilds of Production Environments

    9. Conclusion

  11. Chapter 11 Putting It All Together

    1. Machine Learning Algorithms Revisited

    2. How to Use This Information for Solving Problems

    3. What’s Next for You?