Machine Learning for Hackers - Helion

ebook

Autor: Drew Conway, John Myles White
ISBN: 978-14-493-3053-8
stron: 324, Format: ebook
Data wydania: 2012-02-13
Księgarnia: Helion

Cena książki: 152,15 zł (poprzednio: 176,92 zł)
Oszczędzasz: 14% (-24,77 zł)

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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 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

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Spis treści

Machine Learning for Hackers. Case Studies and Algorithms to Get You Started eBook -- spis treści

Machine Learning for Hackers
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Preface
- Machine Learning for Hackers
- How This Book Is Organized
- Conventions Used in This Book
- Using Code Examples
- Safari Books Online
- How to Contact Us
- Acknowledgements
1. Using R
- R for Machine Learning
  - Downloading and Installing R
    - Windows
    - Mac OS X
    - Linux
  - IDEs and Text Editors
  - Loading and Installing R Packages
  - R Basics for Machine Learning
    - Loading libraries and the data
    - Converting date strings and dealing with malformed data
    - Organizing location data
    - Dealing with data outside our scope
    - Aggregating and organizing the data
    - Analyzing the data
  - 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 Dont Know the Order?
- Ordering Email Messages by Priority
  - Priority Features of Email
- Writing a Priority Inbox
  - Functions for Extracting the Feature Set
  - Creating a Weighting Scheme for Ranking
    - A log-weighting scheme
  - 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
- Clustering Based on Similarity
  - A Brief Introduction to Distance Metrics and Multidirectional Scaling
- How Do US Senators Cluster?
  - Analyzing US Senator Roll Call Data (101st111th Congresses)
    - Exploring senator MDS clustering by Congress
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
Works Cited
- Books
- Articles
Index
About the Authors
Colophon
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