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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Introduction and Foundation
Section 1: Introduction and Foundation
2
Introduction to IBM Cloud
Introduction to IBM Cloud
Understanding IBM Cloud
Accessing the IBM Cloud
Cloud resources
The IBM Cloud and Watson Machine Learning services
Setting up the environment
Watson Studio Cloud
Setting up a new project
Data visualization tutorial
Summary
3
Feature Extraction - A Bag of Tricks
Feature Extraction - A Bag of Tricks
Preprocessing
Dimensional reduction
Data fusion
A bag of tricks
Summary
4
Supervised Machine Learning Models for Your Data
Supervised Machine Learning Models for Your Data
Model selection
Testing the model
Classification
Regression
Testing the predictive capability
Summary
5
Implementing Unsupervised Algorithms
Implementing Unsupervised Algorithms
Unsupervised learning
Semi-supervised learning
Anomaly detection
Online or batch learning
Summary
6
Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
7
Machine Learning Workouts on IBM Cloud
Machine Learning Workouts on IBM Cloud
Watson Studio and Python
Setting up the environment
Data cleansing and preparation
K-means clustering using Python
K-nearest neighbors
Time series prediction example
Summary
8
Using Spark with IBM Watson Studio
Using Spark with IBM Watson Studio
Introduction to Apache Spark
Watson Studio and Spark
Creating a Spark-enabled notebook
Creating a Spark pipeline in Watson Studio
Data preparation
A data analysis and visualization example
Summary
9
Deep Learning Using TensorFlow on the IBM Cloud
Deep Learning Using TensorFlow on the IBM Cloud
Introduction to deep learning
TensorFlow basics
Neural networks and TensorFlow
An example
TensorFlow and image classifications
Additional preparation
Summary
10
Section 3: Real-Life Complete Case Studies
Section 3: Real-Life Complete Case Studies
11
Creating a Facial Expression Platform on IBM Cloud
Creating a Facial Expression Platform on IBM Cloud
Understanding facial expression classification
Exploring expression databases
Preprocessing faces
Preparing the environment
Learning the expression classifier
Summary
12
The Automated Classification of Lithofacies Formation Using ML
The Automated Classification of Lithofacies Formation Using ML
Understanding lithofacies
Exploring the data
Training the classifier
Evaluating the classifier
Summary
13
Building a Cloud-Based Multibiometric Identity Authentication Platform
Building a Cloud-Based Multibiometric Identity Authentication Platform
Understanding biometrics
Exploring biometric data
Feature extraction
Multimodal fusion
Our example
Summary
14
Another Book You May Enjoy
Another Book You May Enjoy
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Testing the predictive capability

Another useful feature of the model builder is that it provides you with the ability to easily test the predictive ability of a deployed model, without having to do any programming.

To test a deployed model from the deployment details page, perform the following steps:

  1. First, in the Test area of the deployment details page, there will be a simple input form (see the following screenshot), where you can type in some values for the feature columns: GENDER, AGE, MARITAL_STATUS, and PROFESSION (you can ignore the other fields in the form):

  1. Next, click on Predict to create a prediction based upon the values that you just entered and the model you have built. This will be the prediction of how much money a customer with the entered attributes is likely to spend on a trip to the store. The following screenshot shows the result of a test. The predicted...
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