Machine Learning For Beginners Guide Algorithms: Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction

by William Sullivan

☆★☆BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook ☆★☆ 200+ pages of valuable content! Machines can LEARN ?!?! Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning Bonus chapters included inside! Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available for beginners. What You'll Learn Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest Neural Networks Python Deep Learning And much, much more! This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field You do not want to miss out on this incredible offer! * For the best visual experience of diagrams it is highly recommend you purchase the paperback version* ☆★☆ Buy your copy now! ☆★☆ ________________________________________ ★☆ Buy the paperback version of this book, and get the kindle version absolutely FREE**

Genres: Programming

268 pages, Kindle Edition

Published August 19, 2017

Reviews

[Amie · Rating 3 out of 5]

2.5 rounded up (although could have rounded down).

Machine Learning for Beginners Guide Algorithms covers basic concepts of machine learning, including an introduction to supervised and unsupervised learning, with a focus on decision trees and random forests (I mean, the title says it all).

I got what I needed out of this book, which was an elementary understanding of the benefits and limitations of random forest algorithms, and a little bit more detail cementing what I knew about supervised and unsupervised learning. However, while the book provides a basic understanding of these topics, the coverage is uneven—some sections dive deep into complex details, while others barely scratch the surface. It’s helpful for a foundational grasp (in most cases), but more consistency of explanations (and even moving some of the really complex detail to an appendix) would really improve the readability and usefulness of this book.

[Dr. Tathagat Varma · Rating 2 out of 5]

A good non-technical discussion on decision trees and random forests that might be useful for beginners. The positive aspect of the book is that is it free of any tech jargon but the negative aspect is that to compensate for it, the book could have used diagrams to help the reader better understand those concepts, and more examples.

[Neil Dunlop · Rating 3 out of 5]

Patchy Content

Mostly a solid overview of machine learning and AI the content is frustratingly superficial in places and assumes prior knowledge in others.

[Fabricio Echeverría]

It's simple and too explained

[Ben · Rating 2 out of 5]

Learned a little - most of the book I already knew. Not that interesting.

1.9/5