Machine Learning For Dummies (2^nd Ed.)

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.

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

John Mueller has produced hundreds of books and articles on topics ranging from networking to home security and from database management to heads-down programming.
Luca Massaron is a senior expert in data science who has been involved with quantitative methods since 2000. He is a Google Developer Expert (GDE) in machine learning.