Drew Conway, John Myles White 지음 | 원서 | 2012년 02월 | OReilly Media
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페이지 : 322쪽 | ISBN : 9781449303716 | 난이도 : 중/고급 | 변환코드 : 8371
부록 : 없음
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 naive 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
Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.
John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.
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 Colophon