Pratap Dangeti - Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
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- Book:Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
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Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R: summary, description and annotation
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- Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
- Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
- Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.
Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.
By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.
What you will learn- Understand the Statistical and Machine Learning fundamentals necessary to build models
- Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems
- Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages
- Analyze the results and tune the model appropriately to your own predictive goals
- Understand the concepts of required statistics for Machine Learning
- Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models
- Learn reinforcement learning and its application in the field of artificial intelligence domain
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his masters degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Table of Contents- Journey from Statistics to Machine Learning
- Parallelism of Statistics and Machine Learning
- Logistic Regression vs. Random Forest
- Tree-Based Machine Learning models
- K-Nearest Neighbors & Naive Bayes
- Support Vector Machines & Neural Networks
- Recommendation Engines
- Unsupervised Learning
- Reinforcement Learning
Pratap Dangeti: author's other books
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BIRMINGHAM - MUMBAI
Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2017
Production reference: 1180717
ISBN 978-1-78829-575-8
www.packtpub.com
Author Pratap Dangeti | Copy Editor Safis Editing |
Reviewer Manuel Amunategui | Project Coordinator Nidhi Joshi |
Commissioning Editor Veena Pagare | Proofreader Safis Editing |
Acquisition Editor Aman Singh | Indexer Tejal Daruwale Soni |
Content Development Editor Mayur Pawanikar | Graphics Tania Dutta |
Technical Editor Dinesh Pawar | Production Coordinator Arvindkumar Gupta |
Pratap Dangetidevelops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
I would like to thank my acquisition editor, Aman Singh, and content development editor, Mayur Pawanikar, who chose me to write this book and encouraged me constantly throughout the period of writing with their invaluable feedback and input.
Manuel Amunateguiis vice president of data science at SpringML, a startup offering Google Cloud TensorFlow and Salesforce enterprise solutions. Prior to that, he worked as a quantitative developer on Wall Street for a large equity-options market-making firm and as a software developer at Microsoft. He holds master degrees in predictive analytics and international administration.
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