James D. Miller - Hands-On Machine Learning with IBM Watson: Leverage IBM Watson to implement machine learning techniques and algorithms using Python

Copyright 2019 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 or its dealers and distributors, will be held liable for any damages caused or alleged to have been 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.

Commissioning Editor: Sunith Shetty
Acquisition Editor: Yogesh Deokar
Content Development Editor: Athikho Sapuni Rishana
Technical Editor: Vibhuti Gawde
Copy Editor: Safis Editing
Project Coordinator: Kirti Pisat
Proofreader: Safis Editing
Indexer: Priyanka Dhadke
Graphics: Jisha Chirayil
Production Coordinator: Arvindkumar Gupta

First published: March 2019

Production reference: 1280319

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-78961-185-4

www.packtpub.com

Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.

• Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals

• Improve your learning with Skill Plans built especially for you

• Get a free eBook or video every month

• Mapt is fully searchable

• Copy and paste, print, and bookmark content

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at customercare@packtpub.com for more details.

At www.packt.com , you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.

James D. Miller is an innovator and accomplished senior project lead and solution architect with 37 years' experience of extensive design and development across multiple platforms and technologies. Roles include leveraging his consulting experience to provide hands-on leadership in all phases of advanced analytics and related technology projects, providing recommendations for process improvement, report accuracy, the adoption of disruptive technologies, enablement, and insight identification. He has also written a number of books, including Statistics for Data Science; Mastering Predictive Analytics with R,Second Edition; Big Data Visualization; Learning Watson Analytics; and many more.

Mayur Ravindra Narkhede is a researcher with BTech in computer science and an MTech in CSE, specializing in AI. He is a data scientist with core experience in building automated end-to-end solutions, and is proficient at applying technology, AI, ML, data mining, and design thinking to improve business functions with growth profitability. He has worked on multiple advanced solutions for the oil and gas sector, utilities, financial services, road traffic and transport, life science, and big data platforms for asset-intensive industries. He has also played a key role in setting up a data science and big data lab for research and development work. He likes to play badminton, carom, billiards, and goes trekking occasionally, and loves to travel.

If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.

This book serves as a complete guide to becoming well-versed in machine learning on IBM Cloud using Python. You will learn how to build complete machine learning solutions, focusing on the role of data representation and feature extraction.

This book starts with supervised and unsupervised machine learning concepts, including an overview of IBM Cloud and the Watson Machine Learning service. You will learn how to run various techniques, such as k-means clustering, KNN, time series prediction, visual recognition, and text-to-speech in IBM Cloud by means of real-world examples. You will learn how to create a Spark pipeline in Watson Studio. The book will also guide you in terms of deep learning and neural network principles on IBM Cloud with TensorFlow. You will learn how to build chatbots using NLP techniques. Later, you will cover three powerful case studies the facial expression classification platform, the automated classification of lithofacies, and the multibiometric identity authentication platform with a view to becoming well-versed in the methodologies.

By the end of the book, you will be well-positioned to build efficient machine learning solutions on IBM Cloud. You will also be well-equipped with real-world examples to draw insights from the data at hand.

This book is aimed at data scientists and machine learning engineers who would like to get introduced to IBM Cloud and its machine learning services using practical examples.

, Introduction to IBM Cloud , provides a brief introduction to the IBM cloud platform and the machine learning service. Moreover, this chapter provides detailed instructions on how to set up data science and machine learning development environments on IBM Cloud. Finally, it conclude in with an example for loading and visualizing data.