Machine Learning for Beginners: An Introduction for Beginners, Why Machine Learning Matters Today and How Machine Learning Networks, Algorithms, Concepts and Neural Networks Really Work

by Steven Cooper

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If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading.

Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.


Grab your copy today and learn:

The different types of learning algorithm that you can expect to encounter
The numerous applications of machine learning
The future of machine learning
The best practices for picking up machine learning
What languages and libraries to work with
The different types of machine learning and how they differ
The various problems that you can solve with machine learning algorithms
And much more...

Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish.

Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know in no time!

101 pages, Kindle Edition

Published August 12, 2018

About the author

Steven Cooper is a freelance writer, producer, and the author of three previous novels. A former television reporter, he has received multiple Emmy awards and nominations, a national Edward R. Murrow Award, and many honors from the Associated Press. He taught writing at Rollins College (Winter Park, FL) from 2007 to 2012. He currently lives in Atlanta.

Reviews

[Jay Yeo · Rating 5 out of 5]

I came across this as part of recommended resources to start learning about machine learning. I had actually studied some of the concepts within in undergrad but some of the writing really helped to click in place my understanding with the simplicity of the text. I recommend it as a companion resource to something more in-depth for understanding but when you get too thick in the woods you can see here what people are actually trying to achieve with some particular method or concept.

[Martin Ljungqvist · Rating 2 out of 5]

Very brief. Mixes formal and informal language. The definition of Manhattan distance is wrong, should be absolute denoted with || instead of normal parantheses.