Ebook Free , by Peter Bradley
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, by Peter Bradley
Ebook Free , by Peter Bradley
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Product details
File Size: 2993 KB
Print Length: 292 pages
Simultaneous Device Usage: Unlimited
Publication Date: February 25, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07P8T77YB
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Amazon Best Sellers Rank:
#33,800 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
This is an excellent introductory book on Machine Learning. The author has a very intuitive style. This book is definitely for novice researchers and undergraduate students. Therefore, many chapters are not detailed enough to please serious researchers.
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.
This guide explained almost everything you need to know about Python Machine Learning. Through this book, I learned this programming language in a easier and step by step approach. Overall, it was great read!!
One of the best books on probabilistic approaches to machine learning. I like that the author not only describes various techniques but also takes the time to try to pull out the intuitions behind concepts, compares techniques to help the reader understand what makes a technique special and provides a detailed bibliography so that details can be followed up in the original literature. The book covers a huge swath of material from basic theory of Bayesian reasoning all the way to recent work in deep learning models.
This is the first time that I read about the machine learning the books set is comprehensive have data from beginners to advance level. Each and everything defined step by step with the help of figures and diagram. The book is one of the best to make skills in machine learning. I will read this book again and again and follow it in my practical life for guidance.
Great book for machine learning. Tips and tricks for new apps and features are what I really like from this book, also it explains everything very well.
The book is an introduction into machine learning techniques which covers many of the topics without going into many detailed explanations.
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 Bayesian 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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