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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

Time series prediction example

In this section, our goal is to try implementing a time series model using Python and Watson Studio.

Time series analysis includes methods for analyzing time series data (of course) so we can extract meaningful statistics and other characteristics of the data. Time series forecasting is the process where we use an algorithm to predict future values based on previously observed values.

Time series analysis

Time series data may be stationary or non-stationary in nature. Stationary implies flat without periodic fluctuations, while non-stationary data typically has frequent shifts in value. You see time series analysis generally used for non-stationary data, such as evaluating and predicting retail...

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