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…mehr
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
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 Colophon
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
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