| Description |
1 online resource (xiii, 303 pages) : illustrations |
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text file rda |
| Bibliography |
Includes bibliographical references (pages 293-294) and index. |
| Contents |
Table of Contents; 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; 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. |
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Further Reading on RChapter 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; Defining the Classifier and Testing It with Hard Ham. |
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Testing the Classifier Against All Email TypesImproving the Results; Chapter 4. Ranking: Priority Inbox; How Do You Sort Something When You Don't 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; Chapter 5. Regression: Predicting Page Views; Introducing Regression; The Baseline Model; Regression Using Dummy Variables; Linear Regression in a Nutshell. |
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Predicting Web TrafficDefining Correlation; Chapter 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; 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. |
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A Brief Introduction to Distance Metrics and Multidirectional ScalingHow Do US Senators Cluster?; Analyzing US Senator Roll Call Data (101st-111th Congresses); Exploring senator MDS clustering by Congress; Chapter 10. kNN: Recommendation Systems; The k-Nearest Neighbors Algorithm; R Package Installation Data; Chapter 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. |
| Summary |
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 s. |
| Language |
English. |
| Subject |
Computer algorithms.
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Electronic data processing -- Automation.
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Programming languages (Electronic computers)
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Artificial intelligence.
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Computer graphics.
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Electronic Data Processing |
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Programming Languages |
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Social Networking |
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Statistics. |
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Artificial Intelligence |
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Computer Graphics |
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Software |
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Algorithms |
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Algorithmes. |
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Informatique. |
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Langages de programmation. |
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Intelligence artificielle. |
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Infographie. |
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Logiciels. |
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algorithms. |
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artificial intelligence. |
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computer graphics. |
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software. |
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Programming languages (Electronic computers) |
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Computer graphics |
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Artificial intelligence |
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Computer algorithms |
| Added Author |
White, John Myles.
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| In: |
EBL |
| Other Form: |
Print version: Conway, Drew. Machine learning for hackers. 1st ed. Sebastopol, CA : O'Reilly, 2012 9781449303716 (OCoLC)783384312 |
| ISBN |
9781449330545 (electronic bk.) |
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1449330541 (electronic bk.) |
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9781449330538 (electronic bk.) |
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1449330533 (electronic bk.) |
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1306812607 |
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9781306812603 |
| Standard No. |
99951783406 |
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