Books & Videos

Table of Contents

  1. Chapter 1 Introduction to Collective Intelligence

    1. What Is Collective Intelligence?

    2. What Is Machine Learning?

    3. Limits of Machine Learning

    4. Real-Life Examples

    5. Other Uses for Learning Algorithms

  2. Chapter 2 Making Recommendations

    1. Collaborative Filtering

    2. Collecting Preferences

    3. Finding Similar Users

    4. Recommending Items

    5. Matching Products

    6. Building a del.icio.us Link Recommender

    7. Item-Based Filtering

    8. Using the MovieLens Dataset

    9. User-Based or Item-Based Filtering?

    10. Exercises

  3. Chapter 3 Discovering Groups

    1. Supervised versus Unsupervised Learning

    2. Word Vectors

    3. Hierarchical Clustering

    4. Drawing the Dendrogram

    5. Column Clustering

    6. K-Means Clustering

    7. Clusters of Preferences

    8. Viewing Data in Two Dimensions

    9. Other Things to Cluster

    10. Exercises

  4. Chapter 4 Searching and Ranking

    1. What's in a Search Engine?

    2. A Simple Crawler

    3. Building the Index

    4. Querying

    5. Content-Based Ranking

    6. Using Inbound Links

    7. Learning from Clicks

    8. Exercises

  5. Chapter 5 Optimization

    1. Group Travel

    2. Representing Solutions

    3. The Cost Function

    4. Random Searching

    5. Hill Climbing

    6. Simulated Annealing

    7. Genetic Algorithms

    8. Real Flight Searches

    9. Optimizing for Preferences

    10. Network Visualization

    11. Other Possibilities

    12. Exercises

  6. Chapter 6 Document Filtering

    1. Filtering Spam

    2. Documents and Words

    3. Training the Classifier

    4. Calculating Probabilities

    5. A Naïve Classifier

    6. The Fisher Method

    7. Persisting the Trained Classifiers

    8. Filtering Blog Feeds

    9. Improving Feature Detection

    10. Using Akismet

    11. Alternative Methods

    12. Exercises

  7. Chapter 7 Modeling with Decision Trees

    1. Predicting Signups

    2. Introducing Decision Trees

    3. Training the Tree

    4. Choosing the Best Split

    5. Recursive Tree Building

    6. Displaying the Tree

    7. Classifying New Observations

    8. Pruning the Tree

    9. Dealing with Missing Data

    10. Dealing with Numerical Outcomes

    11. Modeling Home Prices

    12. Modeling "Hotness"

    13. When to Use Decision Trees

    14. Exercises

  8. Chapter 8 Building Price Models

    1. Building a Sample Dataset

    2. k-Nearest Neighbors

    3. Weighted Neighbors

    4. Cross-Validation

    5. Heterogeneous Variables

    6. Optimizing the Scale

    7. Uneven Distributions

    8. Using Real Data—the eBay API

    9. When to Use k-Nearest Neighbors

    10. Exercises

  9. Chapter 9 Advanced Classification: Kernel Methods and SVMs

    1. Matchmaker Dataset

    2. Difficulties with the Data

    3. Basic Linear Classification

    4. Categorical Features

    5. Scaling the Data

    6. Understanding Kernel Methods

    7. Support-Vector Machines

    8. Using LIBSVM

    9. Matching on Facebook

    10. Exercises

  10. Chapter 10 Finding Independent Features

    1. A Corpus of News

    2. Previous Approaches

    3. Non-Negative Matrix Factorization

    4. Displaying the Results

    5. Using Stock Market Data

    6. Exercises

  11. Chapter 11 EVOLVING INTELLIGENCE

    1. What Is Genetic Programming?

    2. Programs As Trees

    3. Creating the Initial Population

    4. Testing a Solution

    5. Mutating Programs

    6. Crossover

    7. Building the Environment

    8. A Simple Game

    9. Further Possibilities

    10. Exercises

  12. Chapter 12 Algorithm Summary

    1. Bayesian Classifier

    2. Decision Tree Classifier

    3. Neural Networks

    4. Support-Vector Machines

    5. k-Nearest Neighbors

    6. Clustering

    7. Multidimensional Scaling

    8. Non-Negative Matrix Factorization

    9. Optimization

  1. Appendix Third-Party Libraries

    1. Universal Feed Parser

    2. Python Imaging Library

    3. Beautiful Soup

    4. pysqlite

    5. NumPy

    6. matplotlib

    7. pydelicious

  2. Appendix Mathematical Formulas

    1. Euclidean Distance

    2. Pearson Correlation Coefficient

    3. Weighted Mean

    4. Tanimoto Coefficient

    5. Conditional Probability

    6. Gini Impurity

    7. Entropy

    8. Variance

    9. Gaussian Function

    10. Dot-Products

  3. Colophon