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Publisher's Summary

Two audiobooks in one. Do not miss out on the bundle book offer! What you'll learn:

Book one: Machine Learning

You will learn the fundamentals of machine learning from algorithms, Python, and supervised and unsupervised learning. Concepts such as decision trees and random forest introduction are explained in detail to assist in grasping the subject matter. You will learn real world applications of machine learning (artificial intelligence) and understand how it will affect humanity in the upcoming years.

The world is constantly changing and evolving. Transportation was revolutionized by cars and planes; however, machine learning will revolutionize the world in which we live from simple day-to-day tasks to even the most complex endeavors.

Book two: Markov Models

In this segment of the bundle, you will learn the mathematics behind Markov model algorithms, artificial intelligence, weather reporting, Bayesian inference, tools, solutions, and much, much more! You will gain insights to the three main problems of Markov models and learn how to overcome them. You will also learn about the real world applications, implications, and theories of Markov models.

©2017 Healthy Pragmatic Solutions Inc. (P)2017 Healthy Pragmatic Solutions Inc.

What members say

Average Customer Ratings

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    1 out of 5 stars

Awful narration and awful writing<br />

I had to stop less than 30 minutes in. I wanted an informative read for something in my field to listen too. The narrator has an absolutelt annoying voice and a one point in the beginning, the author atarts 5 sentences in a row with "however"

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empty learning

it contribute to me no real data. in 2min search on Google I would get more.

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Not meant to be an audio book

What disappointed you about Machine Learning & Markov Models?

I stopped after the beginning of chapter 3 once I read a review from another person who returned this book, after they reached chapter 5, for the same reason. This book contains a list of terms, definitions, and algorithms and explains them in a very dry way. I was hoping for an explanation of what machine learning is and maybe even how it is used. This book provided a brief overview of that, but then went into harsh detail about the types of algorithms associated with machine learning.

My initial thought before purchasing this book was - if this book were to dive into detail, which it did, it would probably do so in a way where the information could be learned audibly, which it didn't. This is probably a great book to read, but I wouldn't recommend it for listening.

Would you ever listen to anything by William Sullivan again?

Probably not. It seems his books are more meant to be read.

You didn’t love this book... but did it have any redeeming qualities?

It was incredibly informative. It's just simply that the knowledge contained isn't easily absorbed in an audible fashion.

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that was great

Good for understanding machine learning mathematics. It has succinct explanations for equations so most of time you do not need to worry about understanding math

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feel better

Despite its title, this book provides a superb introduction to much of machine learning. Similarly, its treatment of Deep Learning is easily one of the best. The care with which the authors wrote is obvious, starting with systematically defining terms, a mark of true scholarship in my opinion. I can't over-recommend this book. And of course, the authors are all deeply involved (no pun intended) in developing Deep Learning in the first place. It doesn't get any better.

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superb introducttion

This book is a model of technical explanation -- clear, precise, well-organized, and relentlessly helpful. The authors use just the right amount of math to clarify the English text. Even the typesetting is impeccable.

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great overview

Excellent overview. This book is of appropriate depth and breadth and is the first of its kind, i.e., an academic treatment of deep learning. My only complaint is that it doesn't give the reader enough of a chance to solve problems, whether theoretic or hands-on (programming).

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thanks

This books...........books that are able to explain the algorithms and other processes without speaking linear algebra.
Great Book! Very practical guide on Machine Learning.
Thanks

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easy understanding

This book is one of the few machine learning books currently available in the market that provide fully-integrated, fully-working Python implementation codes. The author successfully made tremendous efforts in bringing a variety of sophisticated machine learning algorithms in both classical statistical learning and deep learning by simple, straightforward and clear explanation together with fully-working step-by-step python codes down to average listeners with basic technical understanding in machine learning area

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quick guide

I quickly looked through some deep learning chapters and enjoyed it, it's not complex and the author did nice job to explain it clearly.

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  • rogerbilla
  • 12-14-17

terrific

For what it is, a brief, nontechnical overview of the field of machine learning, including a discussion of employment opportunities and online courses, this s short audible book is terrific.

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  • Meghan
  • 12-14-17

it works


The book has some math. Once again, I think this is included to alert a beginner that understanding and working with math is mandatory to work in this field. The author lists job titles and salary ranges for jobs in the data science/machine learning fields.q

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  • Natasha T Franklin
  • 12-14-17

great level

The core ideas and functions well explained. A book to read first and then digging into specialization. Helps to grasp the essentials and to have a base for the next level

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  • Carious
  • 12-14-17

5 star

This is a fairly good reference for freshman graduate students for many machine learning topics. The materials are well presented

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  • john
  • 12-14-17

nice book

Author explains pretty well the basic aspects and the text is easy to follow even for those not very confident with mathematics. As author claims, everything seems to revolve around approach but this is his choice. I would recommend the book for graduate students doing their work in machine learning domain.

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  • swetha
  • 12-14-17

compulsary

A compulsory book required by statistical machine learning, good for the course and research, but not recommend for practical machine learning.

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  • Tara
  • 12-14-17

awesome

I had heard, but never read it before. And now I repent why I took so long to start it. The material is both rigorous, in-depth and at the same time suitably presented for a beginner with limited mathematical background to start smoothly. Highly recommended!

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  • Rodney Wagner
  • 12-14-17

fabulous


This is explained so as to be helpful as a reference book. Just the other day, I found myself implementing a parallelized feed-forward neural network and quickly picked up the text to see what variations would be helpful. Each topic is generally presented in its basic form with any alternatives and optimizations presented toward the end of the chapter. This meant it was very easy to find what I wanted in seconds.

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  • Marjorie
  • 12-11-17

great follow

This book has helped me progress my baseline understanding of machine learning. The concepts are explained in a smooth fashion way with the right mix of statistical terms. The visual examples and clear instructions made the rest of the book a breeze to follow.

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  • Madeleine B Howard
  • 12-11-17

great overview

The author gives an overview of data science that distinguishes machine learning from data mining, artificial intelligence, etc. I found this useful. He gives specific examples of how machine learning is implemented in marketing and elsewhere. Different types of algorithms, some supported by humans and some not, the machine learning or data mining or other data science. For anyone who is interested in learning the terminology and scope of approaches that comprise machine learning, this book is current, concise, and informative