MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02119aam a2200205 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210629b2021 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781484268667 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
P8P7 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Putatunda, Sayan |
| 9 (RLIN) |
2513417 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Practical machine learning for streaming data with python: design, develop, and validate online learning models |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Name of publisher, distributor, etc. |
Apress Media |
| Date of publication, distribution, etc. |
2021 |
| Place of publication, distribution, etc. |
New York |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 118 p. ill. |
| Other physical details |
Includes bibliographical references and index |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Table of contents<br/><br/>Chapter 1: An Introduction to Streaming Data<br/>Chapter 2: Concept Drift Detection in Data Streams<br/>Chapter 3: Supervised Learning for Streaming Data<br/>Chapter 4: Unsupervised Learning and Other Tools for Data Stream Mining.<br/><br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. <br/>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.<br/>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.<br/><br/>https://www.apress.com/gp/book/9781484268667<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning |
| 9 (RLIN) |
2513418 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python |
| 9 (RLIN) |
2513419 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data stream |
| 9 (RLIN) |
2513420 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer program language |
| 9 (RLIN) |
2513421 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
