Hands-On Machine Learning with IBM Watson
- Paperback: 288 pages
- Publisher: WOW! eBook (March 29, 2019)
- Language: English
- ISBN-10: 1789611857
- ISBN-13: 978-1789611854
Hands-On Machine Learning with IBM Watson: Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services
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.
- 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
By the end of this Hands-On Machine Learning with IBM Watson 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.