Practical Machine Learning for Streaming Data with Python

eBook Details:

• Paperback: 134 pages
• Publisher: WOW! eBook (April 23, 2021)
• Language: English
• ISBN-10: 1484268660
• ISBN-13: 978-1484268667

eBook Description:

Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This Practical Machine Learning for Streaming Data with Python book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights.

You’ll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You’ll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.

What You’ll Learn

• Understand machine learning with streaming data concepts
• Review incremental and online learning
• Develop models for detecting concept drift
• Explore techniques for classification, regression, and ensemble learning in streaming data contexts
• Apply best practices for debugging and validating machine learning models in streaming data context
• Get introduced to other open-source frameworks for handling streaming data

Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The Practical Machine Learning for Streaming Data with Python book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.

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