Redis Cookbook

If you're an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data Machine generated contents note: 1. Using R R for Machine Learning Downloading and Installing R IDEs and Text Editors Loading and Installing R Packages R Basics for Machine Learning Further Reading on R 2. Data Exploration Exploration versus Confirmation What Is Data? Inferring the Types of Columns in Your Data Inferring Meaning Numeric Summaries Means, Medians, and Modes Quantiles Standard Deviations and Variances Exploratory Data Visualization Visualizing the Relationships Between Columns 3. Classification: Spam Filtering This or That: Binary Classification Moving Gently into Conditional Probability Writing Our First Bayesian Spam Classifier Defining the Classifier and Testing It with Hard Ham Testing the Classifier Against All Email Types Improving the Results 4. Ranking: Priority Inbox How Do You Sort Something When You Don't Know the Order? Ordering Email Messages by Priority Contents note continued: Priority Features of Email Writing a Priority Inbox Functions for Extracting the Feature Set Creating a Weighting Scheme for Ranking Weighting from Email Thread Activity Training and Testing the Ranker 5. Regression: Predicting Page Views Introducing Regression The Baseline Model Regression Using Dummy Variables Linear Regression in a Nutshell Predicting Web Traffic Defining Correlation 6. Regularization: Text Regression Nonlinear Relationships Between Columns: Beyond Straight Lines Introducing Polynomial Regression Methods for Preventing Overfitting Preventing Overfitting with Regularization Text Regression Logistic Regression to the Rescue 7. Optimization: Breaking Codes Introduction to Optimization Ridge Regression Code Breaking as Optimization 8. PCA: Building a Market Index Unsupervised Learning 9. MDS: Visually Exploring US Senator Similarity Contents note continued: Clustering Based on Similarity A Brief Introduction to Distance Metrics and Multidirectional Scaling How Do US Senators Cluster? Analyzing US Senator Roll Call Data (101st 111th Congresses) 10. kNN: Recommendation Systems The k-Nearest Neighbors Algorithm R Package Installation Data 11. Analyzing Social Graphs Social Network Analysis Thinking Graphically Hacking Twitter Social Graph Data Working with the Google SocialGraph API Analyzing Twitter Networks Local Community Structure Visualizing the Clustered Twitter Network with Gephi Building Your Own "Who to Follow" Engine 12. Model Comparison SVMs: The Support Vector Machine Comparing Algorithms. If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data "If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research." -- Amazon A balanced introduction to machine learning principles and applications. From the cover: "Case studies and algorithms to get you started".