Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Machine Learning For Dummies
  • Table Of Contents Toc
Machine Learning For Dummies

Machine Learning For Dummies

By : John Paul Mueller, Luca Massaron
close
close
Machine Learning For Dummies

Machine Learning For Dummies

By: John Paul Mueller, Luca Massaron

Overview of this book

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. In the initial chapters, the book introduces you to the world of machine learning, artificial intelligence, big data, and will prepare you to use R and Python for machine learning tasks. Next, you’ll learn how to use math in machine learning and get started with linear models and neural networks. In the final chapters, you’ll process images and text, and discover packages and techniques to improve your machine learning models. By the end of this book, you’ll be able to understand and implement machine learning seamlessly.
Table of Contents (34 chapters)
close
close
2
Part 1: Introducing How Machines Learn
6
Part 2: Preparing Your Learning Tools
12
Part 3: Getting Started with the Math Basics
17
Part 4: Learning from Smart and Big Data
24
Part 5: Applying Learning to Real Problems
28
Part 6: The Part of Tens
31
About the Author
32
Advertisement Page
33
Connect with Dummies
34
End User License Agreement

Chapter 13

Preprocessing Data

IN THIS CHAPTER

Deciding to get more data or start preparations

Fixing bad data like wrong or missing values

Creating new meaningful features

Compressing and reconstructing any redundant information

Understanding why you should beware of outliers

When building a new house, before thinking of any beautiful architecture, aesthetic addition, or even furniture designed to beautify it, you need to build a solid foundation over which to construct walls. In addition, the more difficult the terrain you have to work on, the more time and effort it will take. If you neglect to create a sturdy foundation, nothing built on it can withstand time and nature for long.

The same issue exists in machine learning. No matter the level of sophistication of the learning algorithm, if you don’t prepare your foundation well — that is, your data — your algorithm won’t last long when tested in real data situations. You can’t prepare data by just...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Machine Learning For Dummies
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon