This book is an indispensable guide focuses on Machine Learning and R Programming, in an instructive and conversational tone which helps them who want to make their career in Big Data Analytics/ Data Science and entry level Data Scientist for their day to day tasks with practical examples, detailed description, Issues, Resolutions, key techniques and many more. This book is like your personal trainer, explains the art of Big data Analytics/ Data Science with R Programming in 18 steps which covers from Statistics, Unsupervised Learning, Supervised Learning as well as Ensemble Learning. Many Machine Learning Concepts are explained in an easy way so that you feel confident while using them in Programming. If you are already working as a Data Analyst, still you need this book to sharpen your skills. This book will be an asset to you and your career by making you a better Data Scientist.Download Ebook
One interesting thing in Big Data Analytics, it is the career Option for people with various study backgrounds. I have seen Data Analyst/Business Analyst/Data Scientists with different qualifications like M.B.A, Statistics, M.C.A, M. Tech, M.sc Mathematics and many more. It is wonderful to see people with different backgrounds working on the same project, but how can we expect Machine Learning and Domain knowledge from a person with technical qualification.
Every person might be strong in their own subject but Data Scientist needs to know more than one subject (Programming, Machine Learning, Mathematics, Business Acumen and Statistics). This might be the reason I thought it would be beneficial to have a resource that brings together all these aspects in one volume so that it would help everybody who wants to make Big Data Analytics/ Data Science as their career Option.
This book was written to assist learners in getting started, while at the same time providing techniques that I have found to be useful to Entry level Data Analyst and R programmers. This book is aimed more at the R programmer who is responsible for providing insights on both structured and unstructured data.
This book assumes that the reader has no prior knowledge of Machine Learning and R programming. Each one of us has our own style of approach to an issue; it is likely that others will find alternate solutions for many of the issues discussed in this book. The sample data that appears in a number of examples throughout this book was just an imaginary, any resemblance was simply accidental.
This book was organized in 18 Steps from introduction to Ensemble Learning, which offers the different thinking patterns in Data Scientist work environment. The solutions to some of the questions are not written fully but only some steps of hints are mentioned. It is just for the sake of recalling the memory involving important facts in common practice.
Y. Lakshmi Prasad
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