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Machine learning for hackers /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly,
2012.
|
| Edición: | 1st ed. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Conway, Drew. | |
| 245 | 1 | 0 | |a Machine learning for hackers / |c Drew Conway and John Myles White. |
| 250 | |a 1st ed. | ||
| 260 | |a Sebastopol, CA : |b O'Reilly, |c 2012. | ||
| 300 | |a 1 online resource (xiii, 303 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |2 rda | ||
| 504 | |a Includes bibliographical references (pages 293-294) and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer algorithms. | |
| 650 | 0 | |a Electronic data processing |x Automation. | |
| 650 | 0 | |a Programming languages (Electronic computers) | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer graphics. | |
| 650 | 1 | 2 | |a Electronic Data Processing |
| 650 | 1 | 2 | |a Programming Languages |
| 650 | 1 | 2 | |a Social Networking |
| 650 | 1 | 2 | |a Statistics. |
| 650 | 2 | 2 | |a Artificial Intelligence |
| 650 | 2 | 2 | |a Computer Graphics |
| 650 | 2 | 2 | |a Software |
| 650 | 2 | |a Algorithms | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Informatique. | |
| 650 | 6 | |a Langages de programmation. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Infographie. | |
| 650 | 6 | |a Logiciels. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a computer graphics. |2 aat | |
| 650 | 7 | |a software. |2 aat | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Programming languages (Electronic computers) |2 fast | |
| 650 | 7 | |a Computer graphics |2 fast | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 700 | 1 | |a White, John Myles. | |
| 773 | 0 | |t EBL | |
| 776 | 0 | 8 | |i Print version: |a Conway, Drew. |t Machine learning for hackers. |b 1st ed. |d Sebastopol, CA : O'Reilly, 2012 |z 9781449303716 |w (OCoLC)783384312 |
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