Machine Learning for Beginners

By: William Sullivan

Narrated by: Lukas Arnold

Length: 1 hr and 23 mins

Publisher's Summary

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.

Instead of spending hundreds or even thousands of dollars on courses/materials why not listen to this audiobook instead? It's a worthwhile listen and the most valuable investment you can make for yourself.

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 step-by-step guide in machine learning that exists.

Learn from one of the most reliable programmers alive and expert in the field.

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

I gave up at chapter 5

This was not meant to be an audio book. As others note, the narration is terrible. I'm not sure it wasn't narrated by a computer voice. The paperwork pauses .. are ... way ... to ... long ... between ... sentences. it makes for a very copy flow.

Additionally, the material is superficial, with no substance. lots of lists, with little to no substance to explain the content on the lists. As an audio book there is no way to remember all the list items as you listen.

excellent overview

This is an excellent overview to the complex world of machine learning. I was able to get a beginners understanding of the process well as I learned of this through the audio book. Great overall for helping with a tough knowledge to gain.

Horrible

Probably the worst book I have ever read. No introduction, no context, nothing. Just throws the content on you.

How many sentences can we sting together with lost subjects?

That seems to be main question the author was considering when putting this together. Audio was harsh and had bad room reverb. It did list several machine learning types and ai types. Maybe I should just google this stuff instead.

Don't were your time

I can't believe that they allowed this book to be published. Filled with errors and very poorly done, it failed to provide any useable content. Never again will I purchase any book from this author.

Best ML intro book !

This is the best introduction book I have read in ML. It is very well written, good flow, clear language, easy to the grasp and understand. So much knowledge to absorb. It covers the core concept. I strongly recommend to any out there interested in this domain.

good introduction for me, formulas are not easy

good introduction for me, still there are some formulas that can not be understood if you are driving they need to be a way to be seen or explained in different way.
thanks

it is very specific

The discussion has been very specific, although this is really intended for people who have basic knowledge on machine learning. I’m also planning on consulting data analytics books too to enhance my learning

get started

An astoundingly thought provoking and imaginative collection... Most collections are hit and miss, this one is all hits... Love the unique thought processes and scenarios delivered to us w/ encouragement to consider and cogitate... Stories off center and kilter intriguing and teasing dexterous minds... You’ll wanna give some little, or large, thought once done w/ each... Some won’t like the author’s notes following each, but I dug them tremendously... Narration is very good... Fans will gobble it up, and if you’re a neophyte to the Howey creations this could be an excellent appetizer to get started on your way....

awesome creture

This is terrific material for new, aspiring science fiction writers--it's like attending a writers’ workshop--and it adds depth to the science fiction fan listener’s enjoyment.

helped me

This is a good and quick listen into the popular algos in Data Science. As the title suggests, it is a good intro and offers several resources to dive into the world of Data Science

thank you

The title says it all. I have been introduced to ML as an absolute beginner. Great examples and easy . Thank you

must listen

Technical enough to make me feel like I was learning things but broad enough to let me read through the book without any hiccups

very informative

I've been curious about this subject for a while and recently started to do some research. This book provided a nice high-level understanding of the topic and it's related subject matter.

great overview

This is a fantastic book for the absolute beginner. Although extremely elementary in content, it is very useful for someone with no background

Aweful. Are 5 star reviews fraudulent?

This is book does not deliver on any of the promises made in the synopsis.

Ok but not recommended for a novice. But of a waste of a credit

Narration intonation is not great and makes a hard topic much harder. Punctuation wasn’t really observed and overall this book is not for beginners trying to grasp ML it’s more like a rapid overview for people already pretty steeped in algo’s and mathematical modeling.

WELL EXPLAINED

Highly visual and easy to follow
I'd strongly recommend this book. What I liked about it were the visual explanations of algorithms and the practical explanation of k-means clustering, PCA and linear regression. There's not the same detailed guide for the chapter on neutral networks which was a shame. but it is an absolute beginner's book after all.

useful book

The book is short but I think that's it's strong suit. I suspect it's usefulness will wear off as this information becomes more foundational knowledge through my research into ML but as a beginner in the field, I find that having an easy to navigate reference book for the algorithms I need to learn to become an ML expert is very useful

machine learning

As a beginner I felt the pace of the book was very manageable and each concept is well explained with clear points, often backed up with visual illustrations. You get a good context on what is ML, the algorithms that power ML, and guidance on further learning careers. This book is not a substitute for a textbook but would be a nice complementary resource for anyone starting out in this subject. I feel that business folks or journalists who don’t have a lot of time to sit down and learn this advanced field and who need a rapid snapshot of ML would do well to this book. As a title geared to ‘absolute beginners’, I would not recommend to more advanced stage learners to this book. For me though it hit the spot. Would look forward to a second title in this series if there is one.