Conway D., White J.M. - Machine Learning for Hackers [2012, PDF, ENG]

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Machine Learning for Hackers

Год: 2012
Автор: Drew Conway, John Myles White
Издательство: O'Reilly Media
ISBN: 1449303714
Серия: O'Reilly Media
Язык: Английский
Формат: PDF
Качество: Изначально компьютерное (eBook)
Интерактивное оглавление: Да
Количество страниц: 324
Описание: 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 text
- Use linear regression to predict the number of page views for the top 1,000 websites
- Learn optimization techniques by attempting to break a simple letter cipher
- Compare and contrast U.S. Senators statistically, based on their voting records
- Build a “whom to follow” recommendation system from Twitter data
[spoiler="Примеры страниц"]
http://cdn.oreilly.com/oreilly/booksamplers/9781449303716_Sampler.pdf

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[spoiler="Оглавление"]
Chapter 1 Using R R for Machine Learning Chapter 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 Chapter 3 Classification: Spam Filtering This or That: Binary Classification
Moving Gently into Conditional Probability
Writing Our First Bayesian Spam Classifier Chapter 4 Ranking: Priority Inbox How Do You Sort Something When You Don’t Know the Order?
Ordering Email Messages by Priority
Writing a Priority Inbox Chapter 5 Regression: Predicting Page Views Introducing Regression
Predicting Web Traffic
Defining Correlation Chapter 6 Regularization: Text Regression Nonlinear Relationships Between Columns: Beyond Straight Lines
Methods for Preventing Overfitting
Text Regression Chapter 7 Optimization: Breaking Codes Introduction to Optimization
Ridge Regression
Code Breaking as Optimization Chapter 8 PCA: Building a Market Index Unsupervised Learning
Chapter 9 MDS: Visually Exploring US Senator Similarity
Clustering Based on Similarity
How Do US Senators Cluster? Chapter 10 kNN: Recommendation Systems The k-Nearest Neighbors Algorithm
R Package Installation Data Chapter 11 Analyzing Social Graphs Social Network Analysis
Hacking Twitter Social Graph Data
Analyzing Twitter Networks Chapter 12 Model Comparison SVMs: The Support Vector Machine
Comparing Algorithms
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