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You're reading from Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python

Product type Paperback

Published in Mar 2019

Publisher Packt

ISBN-13 9781789611854

Length 288 pages

Edition 1st Edition

Languages

Python

Tools

IBM Watson

Concepts

Machine Learning

Author (1):

James D. Miller

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Table of Contents (15) Chapters

Preface

1. Section 1: Introduction and Foundation

2. Introduction to IBM Cloud FREE CHAPTER

3. Feature Extraction - A Bag of Tricks

4. Supervised Machine Learning Models for Your Data

5. Implementing Unsupervised Algorithms

6. Section 2: Tools and Ingredients for Machine Learning in IBM Cloud

7. Machine Learning Workouts on IBM Cloud

8. Using Spark with IBM Watson Studio

9. Deep Learning Using TensorFlow on the IBM Cloud

10. Section 3: Real-Life Complete Case Studies

11. Creating a Facial Expression Platform on IBM Cloud

12. The Automated Classification of Lithofacies Formation Using ML

13. Building a Cloud-Based Multibiometric Identity Authentication Platform

14. Another Book You May Enjoy

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Section 3: Real-Life Complete Case Studies

This part of the book provides complete case studies on how to tackle complete, production-ready machine learning problems from A to Z.

The following chapters will be covered in this section:

Chapter 8, Creating a Facial Expression Platform on IBM Cloud
Chapter 9, The Automated Classification of Lithofacies Formation Using ML
Chapter 10, Building a Cloud-Based Multibiometric Identity Authentication Platform
Chapter 11, Conclusion

The rest of the chapter is locked

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Authors (1)

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

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