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Machine Learning for Hackers : Case Studies and Algorithms to Get You Started by John Myles White and Drew Conway (2012, Trade Paperback)

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Machine Learning for Hackers, Paperback by Conway, Drew; White, John Myles, ISBN 1449303714, ISBN-13 9781449303716, Brand New, Free shipping in the US Presents algorithms that enable computers to train themselves to automate tasks, focusing on specific problems such as prediction, optimization, and classification.

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Product Information

Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine learning and statistics tools in a practical fashion, using black-box solutions and case studies instead of a traditional math-heavy presentation. By exploring each problem in this book in depth - including both viable and hopeless approaches - you'll learn to recognize when your situation closely matches traditional problems. Then you'll discover how to apply classical statistics tools to your problem. Machine Learning for Hackers is ideal for programmers from private, public, and academic sectors.

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1449303714
ISBN-13
9781449303716
eBay Product ID (ePID)
110981827

Product Key Features

Author
John Myles White, Drew Conway
Publication Name
Machine Learning for Hackers : Case Studies and Algorithms to Get You Started
Format
Trade Paperback
Language
English
Publication Year
2012
Type
Textbook
Number of Pages
322 Pages

Dimensions

Item Length
9.1in
Item Height
0.7in
Item Width
7in
Item Weight
20.2 Oz

Additional Product Features

Lc Classification Number
Q325.5
Table of Content
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;Colophon;
Copyright Date
2012
Target Audience
Scholarly & Professional
Topic
Machine Theory, Programming / Algorithms, Natural Language Processing
Dewey Decimal
006.31
Dewey Edition
23
Illustrated
Yes
Genre
Computers

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Machine Learning for Hackers : Case Studies and Algorithms to Get You Started by John Myles White and Drew Conway (2012, Trade Paperback)

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item 1 Machine Learning for Hackers: Case - Paperback, by Conway Drew White - Good

item 2 Machine Learning for Hackers: Case Studies and Algorithms to Get You - GOOD

item 3 Machine Learning for Hackers: Case ..., White, John Myl

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