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Introduction to IBM Cloud

In this chapter, we will provide a brief introduction to the IBM Cloud platform and the services that it offers, such as machine learning (ML). Moreover, this chapter will provide detailed instructions on how to set up a data science and machine learning development environment on the IBM Cloud. Finally, we will conclude with a project example that involves loading and visualizing data.

We'll break down this first chapter into the following sections:

Understanding IBM Cloud
The IBM Cloud and Watson Machine Learning services
Setting up the environment
Data visualization tutorial

Key benefits

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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