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, 978-93-92591-14-3 PAPERBACK FIRST EDITION , , ,

Now-a-days, Machine Learning is an essential part of many viable applications and research projects, in areas ranging from medical diagnosis and treatment to finding our friends on social networks. In this book, we have discussed from the scratch the Machine Learning, it’s types. Currently along with other languages, Python is the most preferred language to deal with implementation of Machine Learning & related applications. So, in this book we have given a brief overview of Python Language with small codes to understand the concepts. We have also discussed some important Machine Learning & Data Science Libraries. The applications of machine learning are endless and, with the amount of data available today, mostly limited by your imagination.

This book is for current and aspiring machine learning practitioners looking to understand the basic concepts of Machine Learning. This is an introductory book requiring no previous knowledge of machine learning or artificial intelligence (AI). We focus on using Python and the other libraries, and work through all the steps to create a successful machine learning application. You will get the most out of the book if you are somewhat familiar with Python and the associated libraries.

We made a conscious effort not to focus too much on the math, but rather on the practical aspects of using machine learning algorithms. As mathematics (probability theory, in particular) is the foundation upon which machine learning is built, we won’t go into the analysis of the algorithms in great detail.

There are many books on machine learning and AI. However, all of them are meant for post graduate students or PhD students in computer science, and they’re full of advanced mathematics. This is in stark contrast with how machine learning is being used, as a commodity tool in research and other applications. However, there are few resources out there that fully cover all the important aspects of implementing machine learning in practice, without requiring you to take advanced math courses. We hope this book will help people who want to apply machine learning without reading up on years’ worth of calculus, linear algebra, and probability theory.



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