|
Latest Articles
|
 |
Practical Machine Learning Foring Data With Python (2021) torrent |
| Download torrent: |
|
| Info hash: |
E295973E8A5D4BC75409DB843D51B0E697EC8502 |
| Category: |
Categories >
Books torrents
> Ebooks torrents
|
| Trackers: |
udp://tracker.coppersurfer.tk:6969/announce
udp://9.rarbg.me:2850/announce
udp://9.rarbg.to:2920/announce
udp://tracker.opentrackr.org:1337
udp://tracker.leechers-paradise.org:6969/announce
|
|
| Health: |
|
| Torrent language: |
|
| Total Size: |
7.04 MB |
| Number of files: |
|
| Uploader: |
|
| Torrent added: | 2021-06-22 14:45:06 |
|
Torrent Description
Author: Sayan Putatunda Full Title: Practical Machine Learning For Streaming Data With Python: Design, Develop, And Validate Online Learning Models Publisher: Apress; 1st ed. edition (April 9, 2021) Year: 2021 ISBN-13: 9781484268674 (978-1-4842-6867-4), 9781484268667 (978-1-4842-6866-7) ISBN-10: 1484268679, 1484268660 Pages: 118 Language: English Genre: Educational: Machine Learning File type: EPUB (True), PDF (True), Code Files
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.
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.
Features: ✓ 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
Who This Book Is For: Machine learning engineers and data science professionals.
-Wolves
Related Torrents
|