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Machine Learning Basics

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This book is a "plain-speak" introduction to Machine Learning. It introduces the concepts of unsupervised learning and supervised learning and explains how they differ. It covers clustering methods (K-means, Hierarchical), the Nearest Neighbors algorithm, Decision Trees, Linear Regression, Probabilistic Classification, and Reinforcement Learning.

File Size: 41 KB

Print Length: 16 pages

Publication Date: March 4, 2013

Sold by:  Digital Services LLC

Language: English

ASIN: B00BPEJAPQ

Text-to-Speech: Enabled

X-Ray: Not Enabled

Word Wise: Not Enabled

Lending: Enabled

Enhanced Typesetting: Not Enabled

Best Sellers Rank: #370,749 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #43 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing #74 in Kindle Store > Kindle Short Reads > 30 minutes (12-21 pages) > Computers & Technology #346 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics

This is just a short, inexpensive intro piece. It is good for its purpose. I give it three stars because it is a little math heavy without much explanation, but it does cover some of the major components/methods of machine learning providing you are up to the match included.

I can get more accurate and useful information from Wikipedia. One of the gems "a decision tree is 'a tree of decisions'".

This short book gives a decent overview of the various machine learning algorithms. The examples often lose themselves in quickly defined notations. If you are used to mathematical notations, this fact will not bother you. If you aren't you will likely find the examples confusing.

This book was a very clear, concise text containing exactly what you want to know about ML basics. The format is as follows:- Conceptual introduction.- Algorithm description- Example.Each of these is no more than a page. I want to author a text this good.

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