Drew Conway, John Myles White

Machine Learning for Hackers

• Broschiertes Buch

Produktbeschreibung

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 na

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 300
• Erscheinungstermin: 20. März 2012
• Englisch
• Abmessung: 237mm x 182mm x 22mm
• Gewicht: 530g
• ISBN-13: 9781449303716
• ISBN-10: 1449303714
• Artikelnr.: 34197207

Autorenporträt

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.

Inhaltsangabe

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
Chapter 1: Using R
1.1 R for Machine Learning
Chapter 2: Data Exploration
2.1 Exploration versus Confirmation
2.2 What Is Data?
2.3 Inferring the Types of Columns in Your Data
2.4 Inferring Meaning
2.5 Numeric Summaries
2.6 Means, Medians, and Modes
2.7 Quantiles
2.8 Standard Deviations and Variances
2.9 Exploratory Data Visualization
2.10 Visualizing the Relationships Between Columns
Chapter 3: Classification: Spam Filtering
3.1 This or That: Binary Classification
3.2 Moving Gently into Conditional Probability
3.3 Writing Our First Bayesian Spam Classifier
Chapter 4: Ranking: Priority Inbox
4.1 How Do You Sort Something When You Don't Know the Order?
4.2 Ordering Email Messages by Priority
4.3 Writing a Priority Inbox
Chapter 5: Regression: Predicting Page Views
5.1 Introducing Regression
5.2 Predicting Web Traffic
5.3 Defining Correlation
Chapter 6: Regularization: Text Regression
6.1 Nonlinear Relationships Between Columns: Beyond Straight Lines
6.2 Methods for Preventing Overfitting
6.3 Text Regression
Chapter 7: Optimization: Breaking Codes
7.1 Introduction to Optimization
7.2 Ridge Regression
7.3 Code Breaking as Optimization
Chapter 8: PCA: Building a Market Index
8.1 Unsupervised Learning
Chapter 9: MDS: Visually Exploring US Senator Similarity
9.1 Clustering Based on Similarity
9.2 How Do US Senators Cluster?
Chapter 10: kNN: Recommendation Systems
10.1 The k-Nearest Neighbors Algorithm
10.2 R Package Installation Data
Chapter 11: Analyzing Social Graphs
11.1 Social Network Analysis
11.2 Hacking Twitter Social Graph Data
11.3 Analyzing Twitter Networks
Chapter 12: Model Comparison
12.1 SVMs: The Support Vector Machine
12.2 Comparing Algorithms
Works Citedbooks and publicationsbibliography ofresourcesbooks and publications
website resourcesstatisticsresources formachine learningresources forR programming languageresources for
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