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

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

English | 2021 | ISBN: 978-1484268667 | 134 Pages | PDF, EPUB | 10 MB

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This 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.

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

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.
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