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

Learning the expression classifier

Once you notice that the model status (indicated at the upper-right of the Default Custom Model page) has changed to Model is ready to train, you can then click on the Train Model button to start training the Face model on the training images we provided:

Since we provided only roughly 30 training images, the training process should take less than 5 or 10 minutes. During the training, you will not be able to make any changes to the model or classes.

Evaluating the expression classifier

Once the model training is complete, you should see the following Training successful message:

From this point, you can click on the here hyperlink in the popup to view and test the model.

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