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
John Paul Mueller is a prolific freelance author and technical
editor. He's covered everything from networking and home security
to database management and heads-down programming.
Luca Massaron is a data scientist who specializes in organizing and
interpreting big data, turning it into smart data with data mining
and machine learning techniques.
"Comprehensive and not just for dummies." (MagPi, January 2017)
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