Book Image

Hands-On Machine Learning with IBM Watson

By : James D. Miller
Book Image

Hands-On Machine Learning with IBM Watson

By: James D. Miller

Overview of this book

IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Introduction and Foundation
6
Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
10
Section 3: Real-Life Complete Case Studies

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...