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Machine Learning
Dig deep into the data with a hands-on guide to machine learning Machine Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
408 pages, Paperback
First published October 20, 2014
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53 people want to read
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Displaying 1 - 3 of 3 reviews
September 15, 2016
The book covers several data mining and machine learning algorithms using the Weka toolkit and Eclipse IDE. While I have learned how to use Weka from this book, it is really not the best source to learn even the basic theory behind the algorithms covered. Even the section about probability and Bayes theorem is not explained well. I have pretty good understanding of machine learning algorithms and would recommend that you learn these from somewhere else. For the basics, I recommend machine learning with R.
The book covers several big data tools, such as Hadoop and mapreduce, Spring XD, and Spark. Again, mapreduce is not explained well. But, I have to say that if you don't know how to start Hadoop or Spring or Spark on your machine, this book is a very good start. Unfortuanetly, the there isn't much about how to perform machine learning tasks using these tools.
Finally, there are several errors in the code provided throughout the book.
The book covers several big data tools, such as Hadoop and mapreduce, Spring XD, and Spark. Again, mapreduce is not explained well. But, I have to say that if you don't know how to start Hadoop or Spring or Spark on your machine, this book is a very good start. Unfortuanetly, the there isn't much about how to perform machine learning tasks using these tools.
Finally, there are several errors in the code provided throughout the book.
March 16, 2019
Not badly written, but it explains almost every concept with a different tool. Should the reader learn a dozen of complex tools to understand a dozen of machine learning techniques? Shouldn't the author explain concepts in a tool-agnostic way instead?
March 1, 2017
This book attempted to cover so many things with very little or at times no explanation on theory. Not recommended for someone completely new to machine learning. This book is meant for someone who already has a decent knowledge on ML and looking forward to dive into it using the existing tools / libraries.
Displaying 1 - 3 of 3 reviews
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