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Machine Learning for Hackers

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Machine Learning for Hackers

Drew Conway, John Myles White 지음 | 원서 | 2012년 02월 | OReilly Media

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http://www.hanbit.co.kr/trackback/9781449303716

페이지 : 322쪽 | ISBN : 9781449303716 | 난이도 : 중/고급 | 변환코드 : 8371

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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 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
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저자소개

Drew Conway, John Myles White
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.


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목차

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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https://github.com/johnmyleswhite/ML_for_Hackers

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