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!

From the Back Cover

Learn how machine learning algorithms are invaluable
Implement machine learning in Python and R
Use machine learning to accomplish practical tasks

Machine learning made easy!

It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things using common algorithms, and more!

Inside...

The real story about AI
R and Python coding
The role of statistics
Managing big data
Demystifying the math
Using linear models
What neural networks do
How to work with images
More tools you can use

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