
Programming Collective Intelligence
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Content
- 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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