One of Mark Cuban's top reads for better understanding A.I. (inc.com, 2021)Your comprehensive entry-level guide to machine learningWhile 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 problemsWhether 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 1Part 1: Introducing How Machines Learn 5Chapter 1: Getting the Real Story about AI 7Chapter 2: Learning in the Age of Big Data 23Chapter 3: Having a Glance at the Future 37Part 2: Preparing Your Learning Tools 47Chapter 4: Installing a Python Distribution 49Chapter 5: Beyond Basic Coding in Python 67Chapter 6: Working with Google Colab 87Part 3: Getting Started with the Math Basics 115Chapter 7: Demystifying the Math Behind Machine Learning 117Chapter 8: Descending the Gradient 139Chapter 9: Validating Machine Learning 153Chapter 10: Starting with Simple Learners 175Part 4: Learning from Smart and Big Data 197Chapter 11: Preprocessing Data 199Chapter 12: Leveraging Similarity 221Chapter 13: Working with Linear Models the Easy Way 243Chapter 14: Hitting Complexity with Neural Networks 271Chapter 15: Going a Step Beyond Using Support Vector Machines 307Chapter 16: Resorting to Ensembles of Learners 319Part 5: Applying Learning to Real Problems 339Chapter 17: Classifying Images 341Chapter 18: Scoring Opinions and Sentiments 361Chapter 19: Recommending Products and Movies 383Part 6: The Part of Tens 405Chapter 20: Ten Ways to Improve Your Machine Learning Models 407Chapter 21: Ten Guidelines for Ethical Data Usage 415Chapter 22: Ten Machine Learning Packages to Master 423Index 431