Your no-nonsense guide to making sense of machine learning
Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.
Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly.
- Grasp how day-to-day activities are powered by machine learning
- Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis
- Learn to code in R using R Studio
- Find out how to code in Python using Anaconda
Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
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.
"Comprehensive and not just for dummies." (MagPi, January 2017)
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