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Hands-On Machine Learning with IBM Watson

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

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789611854
Length 288 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Toc

Table of Contents (15) Chapters Close

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

Preparing the environment

Let's now get moving along with the project model development.

The next step (assuming you have already created a new IBM Watson Studio project) is to associate the Watson Visual Recognition Service to the project. We covered how to do this in Chapter 7, Deep Learning Using TensorFlow on the IBM Cloud, so we will assume you have already added the service to this new project. If not, review Chapter 7, Deep Learning Using TensorFlow on the IBM Cloud, or the online Watson Studio documentation.

Project assets

In this chapter's project, our assets will primarily be the training images we have collected and, indirectly perhaps, classified. These image assets are added in a way similar to the...

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