Machine Learning

An Essential Guide to Machine Learning for Beginners Who Want to Understand Applications, Artificial Intelligence, Data Mining, Big Data and More

De: Herbert Jones

Narrado por: Timothy Burke

If you're at the beginner's stage and are looking to gain new knowledge about machine learning, then keep reading....

Do not listen to this audiobook if you're looking for a boring textbook containing a lot of dry math and programming lingo.

Every day, someone is giving up on learning about machine learning. Many people miss out on furthering their career, and perhaps even the progress of our species without even realizing

You see, most beginners make the same mistakes when first delving into the topic of machine learning. They start off with a resource containing too many unrelatable facts, math, and programming lingo that will put them to sleep rather than ignite their passion.

But that is all about to change.

This new book on machine learning will explain the concepts, methods, and history behind machine learning, including how our computers became vastly more powerful but infinitely less intelligent than ever before and why every tech company and their grandmother want to keep track of us 24/7, siphoning data points from our electronic devices to be crunched by their programs that then try to become virtual crystal balls, predicting our thoughts before we even have them.

Most of the book seems like science-fiction because, in a sense, it is, far beyond what an average person would be willing to believe is happening.

Here are some of the topics that are discussed in this book:

• What machine learning is
• What the point of machine learning is
• History of machine learning
• Neural networks
• Matching the human brain
• Artificial Intelligence
• AI in literature
• Talking, walking robots
• Self-driving cars
• Personal voice-activated assistants
• Data mining
• Social networks
• And much more!

Listen now to learn more!

©2018 Herbert Jones (P)2018 Herbert Jones

A great experienced author about ML.

Generally, I am very satisfied with this book. Beforehand I have been speechless with the math and this helped draw me back out a few steps to better understand the concepts.

Best ML book for learners.

This is the best machine learning book I have ever read!!! The author breaks things done into simple easy to understand components. I now have a better understanding of what the Machine Learning engineers are discussing.

Exactly the right level for me

This is the first Machine Learning book I’ve read that didn’t rocket right past me in the third chapter. The author knows their stuff, but doesn’t mind slowing down and explaining what it all means in a well thought out way.

A best and easy way to learn.

I was in search for a good and easy book and one of the my friend told me about this one and he also send me a promo for this book as well. I got it very nice and easy book. There is much information about learning the Machine Learning.

Nothing new, no insights

No insights if you follow the news about the subject. A bit more superficial than what I was expecting.

Good for people that want to know ML.

Easy to understand, good for people that want to know machine learning. This provides an excellent overview of the major techniques. It's definitely worth reading.

Its good book, good informative stuff.

I found it just wasn't hands-on enough. The information was good, but the lack of worked examples or exercises left me feeling like I hadn't learned all that much. I'd say if you're someone who can read information and absorb it, this could be a great introduction to the subject. But if you're like me and need a sandbox, you might want to keep looking.

The book is also quite practical.

This book is ideal for people first coming into Machine Learning looking for a clear high-level introduction. Having said that, the book is also quite practical too, and I had good fun going through the example exercises to build regression lines and scrub data-sets.

Excellent getting started book.

I enjoyed the listen-ability of this book. The emphasis is on how to use and understand Machine Learning rather than the math behind the algorithms. Was an enjoyable read.

A good starting point.

It a good summary book, nevertheless, if the terms are used, they should be defined upfront. If various specific methods are mentioned, it would be good to provide a comparison and overview of those. I general it is good book, which I would recommend reading as a starting point for its structured approach.