Description

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.
Who This Book Is For
Machine learning engineers and data science professionals

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

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Short Description:

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start... Read more

    Publisher: APress
    Publication Date: 09/04/2021
    ISBN13: 9781484268667, 978-1484268667
    ISBN10: 1484268660

    Number of Pages: 118

    Non Fiction , Computing

    Description

    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.
    Who This Book Is For
    Machine learning engineers and data science professionals

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