Intro -- Table of Contents -- Preface -- Prerequisites -- Style of Examples -- Why Python? -- Python Tips -- List and dictionary constructors -- Significant Whitespace -- List comprehensions -- Open APIs -- Overview of the Chapters -- Conventions -- Using Code Examples -- How to Contact Us -- Safari® Books Online -- Acknowledgments -- Introduction to Collective Intelligence -- What Is Collective Intelligence? -- What Is Machine Learning? -- Limits of Machine Learning -- Real-Life Examples -- Other Uses for Learning Algorithms -- Making Recommendations -- Collaborative Filtering -- Collecting Preferences -- Finding Similar Users -- Euclidean Distance Score -- Pearson Correlation Score -- Which Similarity Metric Should You Use? -- Ranking the Critics -- Recommending Items -- Matching Products -- Building a del.icio.us Link Recommender -- The del.icio.us API -- Building the Dataset -- Recommending Neighbors and Links -- Item-Based Filtering -- Building the Item Comparison Dataset -- Getting Recommendations -- Using the MovieLens Dataset -- User-Based or Item-Based Filtering? -- Exercises -- Discovering Groups -- Supervised versus Unsupervised Learning -- Word Vectors -- Pigeonholing the Bloggers -- Counting the Words in a Feed -- Hierarchical Clustering -- Drawing the Dendrogram -- Column Clustering -- K-Means Clustering -- Clusters of Preferences -- Getting and Preparing the Data -- Beautiful Soup -- Scraping the Zebo Results -- Defining a Distance Metric -- Clustering Results -- Viewing Data in Two Dimensions -- Other Things to Cluster -- Exercises -- Searching and Ranking -- What's in a Search Engine? -- A Simple Crawler -- Using urllib2 -- Crawler Code -- Building the Index -- Setting Up the Schema -- Finding the Words on a Page -- Adding to the Index -- Querying -- Content-Based Ranking -- Normalization Function -- Word Frequency.

Document Location -- Word Distance -- Using Inbound Links -- Simple Count -- The PageRank Algorithm -- Using the Link Text -- Learning from Clicks -- Design of a Click-Tracking Network -- Setting Up the Database -- Feeding Forward -- Training with Backpropagation -- Training Test -- Connecting to the Search Engine -- Exercises -- Optimization -- Group Travel -- Representing Solutions -- The Cost Function -- Random Searching -- Hill Climbing -- Simulated Annealing -- Genetic Algorithms -- Real Flight Searches -- The Kayak API -- The minidom Package -- Flight Searches -- Optimizing for Preferences -- Student Dorm Optimization -- The Cost Function -- Running the Optimization -- Network Visualization -- The Layout Problem -- Counting Crossed Lines -- Drawing the Network -- Other Possibilities -- Exercises -- Document Filtering -- Filtering Spam -- Documents and Words -- Training the Classifier -- Calculating Probabilities -- Starting with a Reasonable Guess -- A Naïve Classifier -- Probability of a Whole Document -- A Quick Introduction to Bayes' Theorem -- Choosing a Category -- The Fisher Method -- Category Probabilities for Features -- Combining the Probabilities -- Classifying Items -- Persisting the Trained Classifiers -- Using SQLite -- Filtering Blog Feeds -- Improving Feature Detection -- Using Akismet -- Alternative Methods -- Exercises -- Modeling with Decision Trees -- Predicting Signups -- Introducing Decision Trees -- Training the Tree -- Choosing the Best Split -- Gini Impurity -- Entropy -- Recursive Tree Building -- Displaying the Tree -- Graphical Display -- Classifying New Observations -- Pruning the Tree -- Dealing with Missing Data -- Dealing with Numerical Outcomes -- Modeling Home Prices -- The Zillow API -- Modeling "Hotness" -- When to Use Decision Trees -- Exercises -- Building Price Models -- Building a Sample Dataset.

k-Nearest Neighbors -- Number of Neighbors -- Defining Similarity -- Code for k-Nearest Neighbors -- Weighted Neighbors -- Inverse Function -- Subtraction Function -- Gaussian Function -- Weighted kNN -- Cross-Validation -- Heterogeneous Variables -- Adding to the Dataset -- Scaling Dimensions -- Optimizing the Scale -- Uneven Distributions -- Estimating the Probability Density -- Graphing the Probabilities -- Using Real Data-the eBay API -- Getting a Developer Key -- Setting Up a Connection -- Performing a Search -- Getting Details for an Item -- Building a Price Predictor -- When to Use k-Nearest Neighbors -- Exercises -- Advanced Classification: Kernel Methods and SVMs -- Matchmaker Dataset -- Difficulties with the Data -- Decision Tree Classifier -- Basic Linear Classification -- Categorical Features -- Yes/No Questions -- Lists of Interests -- Determining Distances Using Yahoo! Maps -- Getting a Yahoo! Application Key -- Using the Geocoding API -- Calculating the Distance -- Creating the New Dataset -- Scaling the Data -- Understanding Kernel Methods -- The Kernel Trick -- Support-Vector Machines -- Using LIBSVM -- Getting LIBSVM -- A Sample Session -- Applying SVM to the Matchmaker Dataset -- Matching on Facebook -- Getting a Developer Key -- Creating a Session -- Download Friend Data -- Building a Match Dataset -- Creating an SVM Model -- Exercises -- Finding Independent Features -- A Corpus of News -- Selecting Sources -- Downloading Sources -- Converting to a Matrix -- Previous Approaches -- Bayesian Classification -- Clustering -- Non-Negative Matrix Factorization -- A Quick Introduction to Matrix Math -- What Does This Have to Do with the Articles Matrix? -- Using NumPy -- The Algorithm -- Displaying the Results -- Displaying by Article -- Using Stock Market Data -- What Is Trading Volume? -- Downloading Data from Yahoo! Finance.

Preparing a Matrix -- Running NMF -- Displaying the Results -- Exercises -- Evolving Intelligence -- What Is Genetic Programming? -- Genetic Programming Versus Genetic Algorithms -- Programs As Trees -- Representing Trees in Python -- Building and Evaluating Trees -- Displaying the Program -- Creating the Initial Population -- Testing a Solution -- A Simple Mathematical Test -- Measuring Success -- Mutating Programs -- Crossover -- Building the Environment -- The Importance of Diversity -- A Simple Game -- A Round-Robin Tournament -- Playing Against Real People -- Further Possibilities -- More Numerical Functions -- Memory -- Different Datatypes -- Exercises -- Algorithm Summary -- Bayesian Classifier -- Training -- Classifying -- Using Your Code -- Strengths and Weaknesses -- Decision Tree Classifier -- Training -- Using Your Decision Tree Classifier -- Strengths and Weaknesses -- Neural Networks -- Training a Neural Network -- Using Your Neural Network Code -- Strengths and Weaknesses -- Support-Vector Machines -- The Kernel Trick -- Using LIBSVM -- Strengths and Weaknesses -- k-Nearest Neighbors -- Scaling and Superfluous Variables -- Using Your kNN Code -- Strengths and Weaknesses -- Clustering -- Hierarchical Clustering -- K-Means Clustering -- Using Your Clustering Code -- Multidimensional Scaling -- Using Your Multidimensional Scaling Code -- Non-Negative Matrix Factorization -- Using Your NMF Code -- Optimization -- The Cost Function -- Simulated Annealing -- Genetic Algorithms -- Using Your Optimization Code -- Third-Party Libraries -- Universal Feed Parser -- Installation for All Platforms -- Python Imaging Library -- Installation on Windows -- Installation on Other Platforms -- Simple Usage Example -- Beautiful Soup -- Installation on All Platforms -- Simple Usage Example -- pysqlite -- Installation on Windows.

Installation on Other Platforms -- Simple Usage Example -- NumPy -- Installation on Windows -- Installation on Other Platforms -- Simple Usage Example -- matplotlib -- Installation -- Simple Usage Example -- pydelicious -- Installation for All Platforms -- Simple Usage Example -- Mathematical Formulas -- Euclidean Distance -- Pearson Correlation Coefficient -- Weighted Mean -- Tanimoto Coefficient -- Conditional Probability -- Gini Impurity -- Entropy -- Variance -- Gaussian Function -- Dot-Products -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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