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Machine Learning For Dummies

ISBN: 978-1-119-24577-3
E-book
432 pages
May 2016
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Introduction 1

Part 1: Introducing How Machines Learn 7

CHAPTER 1: Getting the Real Story about AI 9

CHAPTER 2: Learning in the Age? of Big Data 23

CHAPTER 3: Having a Glance at the Future 35

Part 2: Preparing Your Learning Tools 45

CHAPTER 4: Installing an R Distribution 47

CHAPTER 5: Coding in R Using RStudio 63

CHAPTER 6: Installing a Python Distribution 89

CHAPTER 7: Coding in Python Using Anaconda 109

CHAPTER 8: Exploring Other Machine Learning Tools 137

Part 3: Getting Started with the Math Basics 145

CHAPTER 9: Demystifying the? Math Behind Machine Learning 147

CHAPTER 10: Descending the Right Curve 167

CHAPTER 11: Validating Machine Learning 181

CHAPTER 12: Starting with Simple Learners 199

Part 4: Learning from Smart and Big Data 217

CHAPTER 13: Preprocessing Data 219

CHAPTER 14: Leveraging Similarity 237

CHAPTER 15: Working with Linear Models the Easy Way 257

CHAPTER 16: Hitting Complexity with Neural Networks 279

CHAPTER 17: Going a Step beyond Using Support Vector Machines 297

CHAPTER 18: Resorting to Ensembles of Learners 315

Part 5: Applying Learning to Real Problems 331

CHAPTER 19: Classifying Images 333

CHAPTER 20: Scoring Opinions and Sentiments 349

CHAPTER 21: Recommending Products and Movies 369

Part 6: The Part of Tens 383

CHAPTER 22: Ten Machine Learning Packages to Master 385

CHAPTER 23: Ten Ways to Improve Your Machine Learning Models 391

INDEX 399

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