Fundamentals of Machine Learning for Predictive Data Analytics, 2e
ISBN: 9780262364911 | Copyright 2020
Instructor Requests
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| Preface (pg. xv) | |
| Notation (pg. xxiii) | |
| List of Figures (pg. xxxi) | |
| List of Tables (pg. xlvii) | |
| I Introduction to Machine Learning and Data Analytics (pg. 1) | |
| 1 Machine Learning for Predictive Data Analytics (pg. 3) | |
| 2 Data to Insights to Decisions (pg. 23) | |
| 3 Data Exploration (pg. 53) | |
| II Predictive Data Analytics (pg. 115) | |
| 4 Information-Based Learning (pg. 117) | |
| 5 Similarity-Based Learning (pg. 181) | |
| 6 Probability-Based Learning (pg. 243) | |
| 7 Error-Based Learning (pg. 311) | |
| 8 Deep Learning (pg. 381) | |
| 9 Evaluation (pg. 533) | |
| III Beyond Prediction (pg. 595) | |
| 10 Beyond Prediction: Unsupervised Learning (pg. 597) | |
| 11 Beyond Prediction: Reinforcement Learning (pg. 637) | |
| IV Case Studies and Conclusions (pg. 683) | |
| 12 Case Study: Customer Churn (pg. 685) | |
| 13 Case Study: Galaxy Classification (pg. 703) | |
| 14 The Art of Machine Learning for Predictive Data Analytics (pg. 729) | |
| V Appendices (pg. 743) | |
| A Descriptive Statistics and Data Visualization for Machine Learning (pg. 745) | |
| B Introduction to Probability for Machine Learning (pg. 757) | |
| C Differentiation Techniques for Machine Learning (pg. 765) | |
| D Introduction to Linear Algebra (pg. 771) | |
| Bilbiography (pg. 775) | |
| Index (pg. 787) | |
| Instructors Only | |
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