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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Introduction and Foundation

Section 1: Introduction and Foundation

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

Feature Extraction - A Bag of Tricks

Feature Extraction - A Bag of Tricks

Dimensional reduction

A bag of tricks

Supervised Machine Learning Models for Your Data

Supervised Machine Learning Models for Your Data

Model selection

Testing the model

Testing the predictive capability

Implementing Unsupervised Algorithms

Implementing Unsupervised Algorithms

Unsupervised learning

Semi-supervised learning

Anomaly detection

Online or batch learning

Section 2: Tools and Ingredients for Machine Learning in IBM Cloud

Section 2: Tools and Ingredients for Machine Learning in IBM Cloud

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

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

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

TensorFlow and image classifications

Additional preparation

Section 3: Real-Life Complete Case Studies

Section 3: Real-Life Complete Case Studies

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

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

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

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Data preparation

In this step, the data in a DataFrame object is split (using the Spark randomSplit command) into three—a training set (to be used to train a model), a testing set (to be used for model evaluation and testing the assumptions of the model), and a prediction set (used for prediction) and then a record count is printed for each set:

splitted_data=df_data.randomSplit([0.8,0.18,0.02],24)
train_data=splitted_data[0]
test_data=splitted_data[1]
predict_data=splitted_data[2]
print("Number of training records: " + str(train_data.count())) print("Number of testing records : " + str(test_data.count())) print("Number of prediction records : " + str(predict_data.count()))

Executing the preceding commands within the notebook is shown in the following screenshot:

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