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Hands-On Machine Learning with IBM Watson

Hands-On Machine Learning with IBM Watson

By : James D. Miller
1 (1)
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Hands-On Machine Learning with IBM Watson

Hands-On Machine Learning with IBM Watson

1 (1)
By: James D. Miller

Overview of this book

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.
Table of Contents (15 chapters)
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Section 1: Introduction and Foundation
6
Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
10
Section 3: Real-Life Complete Case Studies

Model selection

Machine learning has become more and more ordinary, and understanding which machine learning algorithm (or model type) to use, based upon your data and objectives, is important, and if you are relatively new to the process, it can be daunting.
Fitting a model to training data is one thing, but how do you know that the model (technique) or algorithm you select will generalize well to all your data and create the best prediction? Too much training or overfitting doesn't solve this problem; in fact, in this situation, it is typical for the model to perform poorly with totally new data.

Once again, the IBM Cloud platform provides robust and practical tools to assist you with this process.

The cloud offers a machine learning service (IBM Watson Machine Learning). The service offers the ability to manage your developed machine learning models using a continuous...

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