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Hands-On Machine Learning with IBM Watson

You're reading from  Hands-On Machine Learning with IBM Watson

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

Table of Contents (15) Chapters close

Preface 1. Section 1: Introduction and Foundation
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

Setting up the environment

With IBM Watson Studio, it is a pretty straightforward process to create a Python, Scala, or R notebook. These notebooks can then be used to analyze, clean, and transform data, and perform numerical simulations, statistical modeling, data visualization, machine learning, and other tasks.

To get us going with this chapter's example projects, we need to take the following steps to create a new project and add a notebook in IBM Watson Studio:

  1. Create a new project by first clicking on New project; then, from the Create a project page (shown as follows), find Deep Learning and then click on Create Project:
  1. Next, select a region for the machine learning service to run in then click on Select:

  1. Name your project on the New project page (shown as follows), and then click on Create:
  1. Now that we have created a machine learning project, we are...
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