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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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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction and Foundation FREE CHAPTER
2. Introduction to IBM Cloud 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

Supervised Machine Learning Models for Your Data

This chapter (along with previous two) acts as the backbone for the entire book. It provides a tour of the machine learning paradigm—the features and functionalities available through the IBM Cloud and IBM Watson platforms, with a focus on well-known approaches and algorithms. We'll start the chapter by giving a somewhat practical background to what model evaluation, model selection, and algorithm selection in machine learning entail. Next, we will look at how the IBM Cloud platform can help to simplify and fast-track the entire process.

Moreover, this chapter will discuss machine learning algorithms for classification and regression problems, and again approach these topics using the IBM Cloud platform. By the end of the chapter, the reader should be able to not only understand the concepts involved in selecting an...

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