Practical Machine Learning for Streaming Data with Python

Practical Machine Learning for Streaming Data with Python
PDF, ePUB
  • eBook:
    Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models
  • Author:
    Sayan Putatunda
  • Edition:
    1 edition
  • Categories:
  • Data:
    April 28, 2021
  • ISBN:
    1484268660
  • ISBN-13:
    9781484268667
  • Language:
    English
  • Pages:
    134 pages
  • Format:
    PDF, ePUB

Book 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

Content

Chapter 1: An Introduction to Streaming Data
Chapter 2: Concept Drift Detection in Data Streams
Chapter 3: Supervised Learning for Streaming Data
Chapter 4: Unsupervised Learning and Other Tools for Data Stream Mining

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