Hands-On Machine Learning with IBM Watson
Leverage IBM Watson to implement machine learning techniques and algorithms using Python
- 288 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Hands-On Machine Learning with IBM Watson
Leverage IBM Watson to implement machine learning techniques and algorithms using Python
About This Book
Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services
Key Features
- 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
Book 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
Who this book is for
This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.
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Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Introduction and Foundation
- Introduction to IBM Cloud
- Feature Extraction - A Bag of Tricks
- Supervised Machine Learning Models for Your Data
- Implementing Unsupervised Algorithms
- Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
- Machine Learning Workouts on IBM Cloud
- Using Spark with IBM Watson Studio
- Deep Learning Using TensorFlow on the IBM Cloud
- Section 3: Real-Life Complete Case Studies
- Creating a Facial Expression Platform on IBM Cloud
- The Automated Classification of Lithofacies Formation Using ML
- Building a Cloud-Based Multibiometric Identity Authentication Platform
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