Description
Book SynopsisTable of ContentsIntroduction 1
Part 1: Introducing How Machines Learn 5
Chapter 1: Getting the Real Story about AI 7
Chapter 2: Learning in the Age of Big Data 23
Chapter 3: Having a Glance at the Future 37
Part 2: Preparing Your Learning Tools 47
Chapter 4: Installing a Python Distribution 49
Chapter 5: Beyond Basic Coding in Python 67
Chapter 6: Working with Google Colab 87
Part 3: Getting Started with the Math Basics 115
Chapter 7: Demystifying the Math Behind Machine Learning 117
Chapter 8: Descending the Gradient 139
Chapter 9: Validating Machine Learning 153
Chapter 10: Starting with Simple Learners 175
Part 4: Learning from Smart and Big Data 197
Chapter 11: Preprocessing Data 199
Chapter 12: Leveraging Similarity 221
Chapter 13: Working with Linear Models the Easy Way 243
Chapter 14: Hitting Complexity with Neural Networks 271
Chapter 15: Going a Step Beyond Using Support Vector Machines 307
Chapter 16: Resorting to Ensembles of Learners 319
Part 5: Applying Learning to Real Problems 339
Chapter 17: Classifying Images 341
Chapter 18: Scoring Opinions and Sentiments 361
Chapter 19: Recommending Products and Movies 383
Part 6: The Part of Tens 405
Chapter 20: Ten Ways to Improve Your Machine Learning Models 407
Chapter 21: Ten Guidelines for Ethical Data Usage 415
Chapter 22: Ten Machine Learning Packages to Master 423
Index 431
