Online machine learning: a practical guide with examples in Python

Contributor(s):

Material type: Text

TextSeries: Machine learning: Foundations, Methodologies, and ApplicationsPublication details: Springer Cham SingaporeDescription: xiii, 155 pISBN:

9789819970063

Subject(s):

DDC classification:

006.3 BAR

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. In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository (https://github.com/sn-code-inside/online-machine-learning). The repository is continuously maintained, so the notebooks may change over time. (https://link.springer.com/book/10.1007/978-981-99-7007-0)

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.3 BAR (Browse shelf(Opens below))	Available		008283

Browsing Indian Institute of Management LRC shelves, Shelving location: General Stacks, Collection: IT & Decisions Sciences Close shelf browser (Hides shelf browser)

Previous								Next
Previous	006.3 ASH AI-powered workplace:	006.3 BAI Ai-driven project management: harnessing the power of artificial intelligence and chatgpt to achieve peak productivity and success	006.3 BAN Video based machine learning for traffic intersections	006.3 BAR Online machine learning: a practical guide with examples in Python	006.3 BOR Intelligent automation: welcome to the world of hyper automation	006.3 BRA AI strategy to execution	006.3 BUC From deep learning to rational machines: intelligence what the history of philosophy can teach us about the future of artificial	Next

Table of contents:
Table of contents (11 chapters)
Front Matter
Pages i-xiii
Download chapter PDF
Introduction: From Batch to Online Machine Learning
Thomas Bartz-Beielstein
Pages 1-11
Supervised Learning: Classification and Regression
Thomas Bartz-Beielstein
Pages 13-22
Drift Detection and Handling
Thomas Bartz-Beielstein, Lukas Hans
Pages 23-39
Initial Selection and Subsequent Updating of OML Models
Thomas Bartz-Beielstein
Pages 41-46
Evaluation and Performance Measurement
Thomas Bartz-Beielstein
Pages 47-62
Special Requirements for Online Machine Learning Methods
Thomas Bartz-Beielstein
Pages 63-69
Practical Applications of Online Machine Learning
Steffen Moritz, Florian Dumpert, Christian Jung, Thomas Bartz-Beielstein, Eva Bartz
Pages 71-96
Open-Source Software for Online Machine Learning
Thomas Bartz-Beielstein
Pages 97-104
An Experimental Comparison of Batch and Online Machine Learning Algorithms
Thomas Bartz-Beielstein, Lukas Hans
Pages 105-124
Hyperparameter Tuning
Thomas Bartz-Beielstein
Pages 125-140
Summary and Outlook
Thomas Bartz-Beielstein, Eva Bartz
Pages 141-143
Back Matter
Pages 145-155
[https://link.springer.com/book/10.1007/978-981-99-7007-0]

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.

In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository (https://github.com/sn-code-inside/online-machine-learning). The repository is continuously maintained, so the notebooks may change over time.

(https://link.springer.com/book/10.1007/978-981-99-7007-0)

There are no comments on this title.

to post a comment.