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Full Description
One of Mark Cuban's top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning
While machine learning expertise doesn't quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more.
Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study.
Understand the history of AI and machine learning
Work with Python 3.8 and TensorFlow 2.x (and R as a download)
Build and test your own models
Use the latest datasets, rather than the worn out data found in other books
Apply machine learning to real problems
Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.
Contents
Introduction 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
