Table of Contents

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

About the Author

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.

Reviews

"Comprehensive and not just for dummies." (MagPi, January 2017)