Online Machine Learning : A Practical Guide with Examples in Python / edited by Eva Bartz, Thomas Bartz-Beielstein.

Format:

Book

Author/Creator:

Bartz, Eva.

Contributor:

Bartz-Beielstein, Thomas.

Series:

Machine Learning: Foundations, Methodologies, and Applications, 2730-9916

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Artificial Intelligence.

Machine Learning.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Physical Description:

1 online resource (163 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications. The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs. OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice.

Contents:

Chapter 1:Introduction

Chapter 2:Supervised Learning

Chapter 3:Drift Detection and Handling

Chapter 4:Initial Selection and Subsequent Updating of OML Models

Chapter 5:Evaluation and Performance Measurement

Chapter 6:Special Requirements for OML Methods

Chapter 7:Practical Applications of Online Machine Learning

Chapter 8:Open-Source-Software for Online Machine Learning

Chapter 9:An Experimental Comparison of Batch and Online Machine Learning Algorithms

Chapter 10:Hyperparameter Tuning

Chapter 11:Summary and Outlook.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789819970070

9819970075

OCLC:

1420629919