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© 2021

Practical Machine Learning for Streaming Data with Python

Design, Develop, and Validate Online Learning Models

  • Explains the latest Scikit-Multiflow framework in detail

  • Explains Supervised and Unsupervised Learning for streaming data

  • One of the first books in the market on machine learning models for streaming data using Python

Book
  • 2.1k Downloads

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Sayan Putatunda
    Pages 1-29
  3. Sayan Putatunda
    Pages 31-55
  4. Sayan Putatunda
    Pages 57-96
  5. Back Matter
    Pages 115-118

About this book

Introduction

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.

You will:

  • 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.

Keywords

Machine Learning Python Artificial Intelligence Streaming data Concept Drift Online Learning Real Time Analytics Scikit-Multiflow Apache Kafka

Authors and affiliations

  1. 1.BangaloreIndia

About the authors

Dr. Sayan Putatunda is an experienced data scientist and researcher. He holds a Ph.D. in Applied Statistics/ Machine Learning from the Indian Institute of Management, Ahmedabad (IIMA) where his research was on streaming data and its applications in the transportation industry. He has a rich experience of working in both senior individual contributor and managerial roles in the data science industry with multiple companies such as Amazon, VMware, Mu Sigma, and more. His research interests are in streaming data, deep learning, machine learning, spatial point processes, and directional statistics. As a researcher, he has multiple publications in top international peer-reviewed journals with reputed publishers. He has presented his work at various reputed international machine learning and statistics conferences. He is also a member of IEEE.



Bibliographic information