Implement data science and machine learning techniques to draw insights from real-world data
Understand what IBM Cloud platform can help you to implement cognitive insights within applications
Understand the role of data representation and feature extraction in any machine learning system

Description

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.

What you will learn

Understand key characteristics of IBM machine learning services Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning and neural networks on the IBM Cloud with TensorFlow Create a complete, cloud-based facial expression classification solution Use biometric traits to build a cloud-based human identification system

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Product Details

Publication date : Mar 29, 2019

Length 288 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781789611854

Category :

Data

Languages :

Python (Beginner)

Concepts :

Machine Learning

Tools :

IBM Watson (Beginner)

15 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

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

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

14. Another Book You May Enjoy

Another Book You May Enjoy

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About the author

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

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Hands-On Machine Learning with IBM Watson: Leverage IBM Watson to implement machine learning techniques and algorithms using Python

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