PDF Ebook An Introduction to Machine Learning
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An Introduction to Machine Learning
PDF Ebook An Introduction to Machine Learning
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This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,†how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
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Product details
Hardcover: 291 pages
Publisher: Springer; 1st ed. 2015 edition (July 18, 2015)
Language: English
ISBN-10: 3319200097
ISBN-13: 978-3319200095
Product Dimensions:
6.1 x 0.8 x 9.2 inches
Shipping Weight: 1.6 pounds
Average Customer Review:
3.6 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#2,112,235 in Books (See Top 100 in Books)
Kubat approaches the field of machine learning in a very mathematical yet intuitive way. The book begins with a conceptual understanding of classification through the use of Johnny's pies and continues to step-by-step build upon this knowledge through each chapter. Although there are programming challenges at the end of most chapters, no actual code is used in this book. You must be able to properly program using a mathematical basis to utilize this book to its fullest potential. Kubat smoothly teaches Machine Learning in a comprehensive and conceptual way so that you really understand the field fundamentally.
This is a must read for anyone that wants to get into machine learning. It covers all the main algorithms and practical challenges of building complex models. For a very technical book it is a very easy read, and the questions are a great way to test the understanding of the material learned. The way the material is organized is very interesting too, addressing machine learning from the multiple angles I usually look at things in real life.
I wanted to learn about machine learning, so I started to read this book. I did learn a lot, but I felt that completing the programming assignments was like reinventing the wheel. The descriptions of the algorithms in the book don't give enough practical guidance. You might be able to brute-force your way to a program that works, but why waste your time when these are solved problems? A friend recommended taking an online Stanford University machine learning course taught by Andrew Ng (available from Coursera) and I find the online course to be far more helpful than this book. When I get to the point of needing a good machine learning book, I will definitely buy something else... you can definitely do better.
This is a great introductory book for anyone who is trying to start understanding the basics of machine learning. Unlike many of other machine learning books, this one takes a very simplistic approach and uses simple but very useful examples in each chapter. The chapters are well organized and the problems at the end of each chapter are designed to test your knowledge of the concepts. The programming exercises offer additional opportunities to get deeper into the implementation aspects of the different algorithms and concepts - very valuable if you really try them.When compared to other books, this book really focuses on concepts of machine learning without taking the student through lengthy mathematical proofs. I recommend this book for an introductory course in machine learning and for practitioners who are starting in machine learning. It is a very enjoyable and useful read. You will understand many complex machine learning books after reading this one.Best introduction to machine learning book I have come across - I have many of them.
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