Practical Machine Learning for Streaming Data with Python
Apress (Verlag)
978-1-4842-6866-7 (ISBN)
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
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.
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.
| Erscheinungsdatum | 16.04.2021 |
|---|---|
| Zusatzinfo | 16 Illustrations, black and white; XVI, 118 p. 16 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Apache Kafka • Artificial Intelligence • concept drift • machine learning • Online Learning • Python • Real time analytics • Scikit-Multiflow • Streaming data |
| ISBN-10 | 1-4842-6866-0 / 1484268660 |
| ISBN-13 | 978-1-4842-6866-7 / 9781484268667 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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