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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

Our example

Now that we have an idea and understanding of just what a biometric authentication solution is and how it works, we will try to build a simple but actually working prototype using the same IBM Watson Visual Recognition service we used in Chapter 8, Creating a Facial Expression Platform on IBM Cloud, as well as that project as a guide for our new solution.

As a reminder, the IBM Watson Visual Recognition service understands the image content out-of-the-box (we demonstrated this in our Chapter 8, Creating a Facial Expression Platform on IBM Cloud project). The pretrained models provided enable you to analyze images for objects, faces, colors, food, explicit content, and other subjects for insights into your visual content. We successfully used the service to detect faces and then determine expressions.

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