Download PDF Machine Learning: A Journey from Beginner to Advanced Including Deep Learning, Scikit-learn and Tensorflow
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Machine Learning: A Journey from Beginner to Advanced Including Deep Learning, Scikit-learn and Tensorflow
Download PDF Machine Learning: A Journey from Beginner to Advanced Including Deep Learning, Scikit-learn and Tensorflow
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Product details
Paperback: 312 pages
Publisher: CreateSpace Independent Publishing Platform (July 20, 2018)
Language: English
ISBN-10: 1723484725
ISBN-13: 978-1723484728
Product Dimensions:
8.5 x 0.7 x 11 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
3.9 out of 5 stars
10 customer reviews
Amazon Best Sellers Rank:
#1,047,121 in Books (See Top 100 in Books)
If you have always been curious about the connection between artificial intelligence and the creative contributing influences and have basic programming knowledge, this is the book for you. Machine learning is considered one of the core subjects of AI. This is a great guide towards mastering basics of machine learning and algorithms that will only aide in the improvement of the computer learning from date with limited or no human intervention. Without being too heavy on the computer science jargon, Michael White was able to highlight and clarify many misconceptions. I found the guidance clear and prescriptive. The instructions for accessing different language code were pertinent and relevant, allowing for ease of flow. I was initially discouraged by the use of Python as the programming language of choice, but I then found it to be relatively painless and well suited to the reader a modular view of how these machine learning algorithms work and the potential optimizations when eventually implementing them. Some of the examples required many vector and matrix operations. If your memory of linear algebra is not up to point, I would recommend a crash reminder in the basics because there was frequent reference to long tedious formulas that are expressed in summation notation.
If you have ever been curious about artificial intelligence, or AI, this book will help to understand it with ease. It starts by sharing with the reader the Google invention of an AI conversational agent in 2015. Google has also helped to turn Google Translate into a larger scale AI that can translate anything in a different language simply by the user taking a photo of it. The development of AI is happening much faster than anyone could have ever expected! This book dives into the ways that we can learn more about machine learning and how we can make it a part of our lives.There are three types of machine learning that are brought up in this book. They are supervised, unsupervised, and reinforcement learning. They are broken down in a simple way that every reader of this book will understand.This book is filled with a lot of simple language that people can understand, but there are also quite a few algorithms and mathematical equations that you should try to learn in order to understand this book a little bit better. If you are trying to teach your computer had to answer questions for you with ease, this book is for you. The writer takes the time to explain full details of what machine learning is and how it will help the future of our world.
The book, Machine Learning: A Journey from Beginner to Advanced Including Deep Learning, Scikit-learn, and Tensorflow, is a great introduction to advanced concepts. It covers a lot of information and doesn't really go in depth with the technical stuff. I was looking for a book to give me a foundation of understanding around AI terminology and it definitely did that. Author, Michael B. White, offers a demanding, yet palatable buffet of information. From machine data mining and Python programming to quadratic programming and dimensionality - White covers all the basics. I found the concepts, from around chapter 8 to 15 a little over my head, for the most part, however, I certainly came away with more of an understanding than when I started. I recommend this book to anyone looking for a technical AI: 101 reference guide.
This book may a good tutorial on how to use open source machine learning tool kits, but it didn't make sense to me until I took a course in machine learning. Very early in the book it dives very deeply into the math of regression algorithms without really explaining how they fit in machine learning. Basically it's missing an overview of how ML works. Once I took a course, the book was moot.Also, the author needs to find an editor who understands technical book publishing and is willing to format it for kindle. There were a number of typos that actually lead you to an incorrect conclusion, equations were cut off, no page breaks between pages, etc.
Useful information about machine learning. Here I learned about the concepts that govern these technologies and where they apply. The book is voluminous and covers all important topics about machine learning. After reading, you can begin to apply these skills practically.
Started reading this book to get basics of machine learning. But book organization is such that soon after the basics whole lot of advanced math and algos are presentedBetter would have been having separate sections for the beginner intermediate and advanced pieces so that reader can refer at their own pace.
The content of this book is good. However in the kindle version, the formatting of the examples is not preserved. Since the examples are in python, where formatting is essential to the meaning of the code, this often makes the authors intent unclear and very difficult to read. This seems like a major oversight.
When I started reading this book I thought it would be be a steep read, but it was quite enjoyable. The book starts off by explaining how the rate of AI is growing faster than we can understand, which will be a serious issue for us in the long run. This isn't a book, but a guide on machine learning. Machine learning is the concepts that we give our machines and AI in order to computer things and handle situations. I personally didn't like this book, but it is a good read for anyone who wants to do some self study on AI.
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