Machine Learning For Dummies

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!

Table of Contents

Part 1 Introducing How Machines Learn
Chapter 1 Getting the Real Story about AI
Chapter 2 Learning in the Age of Big Data
Chapter 3 Having a Glance at the Future

Part 2 Preparing Your Learning Tools
Chapter 4 Installing an R Distribution
Chapter 5 Coding in R Using RStudio
Chapter 6 Installing a Python Distribution
Chapter 7 Coding in Python Using Anaconda
Chapter 8 Exploring Other Machine Learning Tools

Part 3 Getting Started with the Math Basics
Chapter 09 Demystifying the Math Behind Machine Learning
Chapter 10 Descending the Right Curve
Chapter 11 Validating Machine Learning
Chapter 12 Starting with Simple Learners

Part 4 Learning from Smart and Big Data
Chapter 13 Preprocessing Data
Chapter 14 Leveraging Similarity
Chapter 15 Working with Linear Models the Easy Way
Chapter 16 Hitting Complexity with Neural Networks
Chapter 17 Going a Step beyond Using Support Vector Machines
Chapter 18 Resorting to Ensembles of Learners

Part 5 Applying Learning to Real Problems
Chapter 19 Classifying Images
Chapter 20 Scoring Opinions and Sentiments
Chapter 21 Recommending Products and Movies

Part 6 The Part of Tens
Chapter 22 Ten Machine Learning Packages to Master
Chapter 23 Ten Ways to Improve Your Machine Learning Models