Programming Collective Intelligence: Building Smart Web 2.0 Applications [Paperback]

Praise for Programming Collective Intelligence; Preface; Prerequisites; Style of Examples; Why Python?; Open APIs; Overview of the Chapters; Conventions; Using Code Examples; How to Contact Us; Safari® Books Online; Acknowledgments; Chapter 1: Introduction to Collective Intelligence; 1.1 What Is Collective Intelligence?; 1.2 What Is Machine Learning?; 1.3 Limits of Machine Learning; 1.4 Real-Life Examples; 1.5 Other Uses for Learning Algorithms; Chapter 2: Making Recommendations; 2.1 Collaborative Filtering; 2.2 Collecting Preferences; 2.3 Finding Similar Users; 2.4 Recommending Items; 2.5 Matching Products; 2.6 Building a del.icio.us Link Recommender; 2.7 Item-Based Filtering; 2.8 Using the MovieLens Dataset; 2.9 User-Based or Item-Based Filtering?; 2.10 Exercises; Chapter 3: Discovering Groups; 3.1 Supervised versus Unsupervised Learning; 3.2 Word Vectors; 3.3 Hierarchical Clustering; 3.4 Drawing the Dendrogram; 3.5 Column Clustering; 3.6 K-Means Clustering; 3.7 Clusters of Preferences; 3.8 Viewing Data in Two Dimensions; 3.9 Other Things to Cluster; 3.10 Exercises; Chapter 4: Searching and Ranking; 4.1 What's in a Search Engine?; 4.2 A Simple Crawler; 4.3 Building the Index; 4.4 Querying; 4.5 Content-Based Ranking; 4.6 Using Inbound Links; 4.7 Learning from Clicks; 4.8 Exercises; Chapter 5: Optimization; 5.1 Group Travel; 5.2 Representing Solutions; 5.3 The Cost Function; 5.4 Random Searching; 5.5 Hill Climbing; 5.6 Simulated Annealing; 5.7 Genetic Algorithms; 5.8 Real Flight Searches; 5.9 Optimizing for Preferences; 5.10 Network Visualization; 5.11 Other Possibilities; 5.12 Exercises; Chapter 6: Document Filtering; 6.1 Filtering Spam; 6.2 Documents and Words; 6.3 Training the Classifier; 6.4 Calculating Probabilities; 6.5 A Naïve Classifier; 6.6 The Fisher Method; 6.7 Persisting the Trained Classifiers; 6.8 Filtering Blog Feeds; 6.9 Improving Feature Detection; 6.10 Using Akismet; 6.11 Alternative Methods; 6.12 Exercises; Chapter 7: Modeling with Decision Trees; 7.1 Predicting Signups; 7.2 Introducing Decision Trees; 7.3 Training the Tree; 7.4 Choosing the Best Split; 7.5 Recursive Tree Building; 7.6 Displaying the Tree; 7.7 Classifying New Observations; 7.8 Pruning the Tree; 7.9 Dealing with Missing Data; 7.10 Dealing with Numerical Outcomes; 7.11 Modeling Home Prices; 7.12 Modeling "Hotness"; 7.13 When to Use Decision Trees; 7.14 Exercises; Chapter 8: Building Price Models; 8.1 Building a Sample Dataset; 8.2 k-Nearest Neighbors; 8.3 Weighted Neighbors; 8.4 Cross-Validation; 8.5 Heterogeneous Variables; 8.6 Optimizing the Scale; 8.7 Uneven Distributions; 8.8 Using Real Data—the eBay API; 8.9 When to Use k-Nearest Neighbors; 8.10 Exercises; Chapter 9: Advanced Classification: Kernel Methods and SVMs; 9.1 Matchmaker Dataset; 9.2 Difficulties with the Data; 9.3 Basic Linear Classification; 9.4 Categorical Features; 9.5 Scaling the Data; 9.6 Understanding Kernel Methods; 9.7 Support-Vector Machines; 9.8 Using LIBSVM; 9.9 Matching on Facebook; 9.10 Exercises; Chapter 10: Finding Independent Features; 10.1 A Corpus of News; 10.2 Previous Approaches; 10.3 Non-Negative Matrix Factorization; 10.4 Displaying the Results; 10.5 Using Stock Market Data; 10.6 Exercises; Chapter 11: EVOLVING INTELLIGENCE; 11.1 What Is Genetic Programming?; 11.2 Programs As Trees; 11.3 Creating the Initial Population; 11.4 Testing a Solution; 11.5 Mutating Programs; 11.6 Crossover; 11.7 Building the Environment; 11.8 A Simple Game; 11.9 Further Possibilities; 11.10 Exercises; Chapter 12: Algorithm Summary; 12.1 Bayesian Classifier; 12.2 Decision Tree Classifier; 12.3 Neural Networks; 12.4 Support-Vector Machines; 12.5 k-Nearest Neighbors; 12.6 Clustering; 12.7 Multidimensional Scaling; 12.8 Non-Negative Matrix Factorization; 12.9 Optimization; Third-Party Libraries; Universal Feed Parser; Python Imaging Library; Beautiful Soup; pysqlite; NumPy; matplotlib; pydelicious; Mathematical Formulas; Euclidean Distance; Pearson Correlation Coefficient; Weighted Mean; Tanimoto Coefficient; Conditional Probability; Gini Impurity; Entropy; Variance; Gaussian Function; Dot-Products; Colophon;