Hands-On Machine Learning with IBM Watson: Leverage IBM Watson to implement machine learning techniques and algorithms using Python
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Book Description
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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.
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Table of Contents
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1. Introduction to IBM Cloud
2. Feature Extraction – A Bag of Tricks
3. Supervised Machine Learning Models for Your Data
4. Implementing Unsupervised Algorithms
5. Machine Learning Workouts on IBM Cloud
6. Using SPARK with IBM Watson Studio
7. Deep Learning Using TensorFlow on the IBM Cloud
8. Creating a Facial Expression Platform on the IBM Cloud
9. Automated Classification of Lithofacies Formation Using Machine Learning
10. Building a Cloud-Based Multi-Biometric Identity Authentication Platform