Machine Learning for Streaming Data with Python | Rapidly build practical online machine learning solutions using River and other top key frameworks

Name: Machine Learning for Streaming Data with Python | Rapidly build practical online machine learning solutions using River and other top key frameworks
Author: Joos Korstanje
ISBN: 9781803248363

Joos Korstanje

Lingua: inglese

Editore: Packt Publishing, 2022

180324836X / 9781803248363

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Titolo: Machine Learning for Streaming Data with Python | Rapidly build practical online machine learning solutions using River and other top key frameworks
Autore: Joos Korstanje
Editore: Packt Publishing
Anno di pubblicazione: 2022
Condizione: Neu
Rilegatura: Taschenbuch
Lingua: inglese
ISBN 10: 180324836X
ISBN 13: 9781803248363
Peso dell'articolo: 489 grammi
Dimensioni: 235 x 191 x 14 mm
Cataloghi dei venditori: Bücher

Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming

Key Features

Work on streaming use cases that are not taught in most data science courses
Gain experience with state-of-the-art tools for streaming data
Mitigate various challenges while handling streaming data

Book Description

Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.

You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.

By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.

What you will learn

Understand the challenges and advantages of working with streaming data
Develop real-time insights from streaming data
Understand the implementation of streaming data with various use cases to boost your knowledge
Develop a PCA alternative that can work on real-time data
Explore best practices for handling streaming data that you absolutely need to remember
Develop an API for real-time machine learning inference

Who this book is for

This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.

An Introduction to Streaming Data
Architectures for Streaming and Real-Time Machine Learning
Data Analysis on Streaming Data
Online Learning with River
Online Anomaly Detection
Online Classification
Online Regression
Reinforcement Learning
Drift and Drift Detection
Feature Transformation and Scaling
Catastrophic Forgetting
Conclusion and Best Practices

"Riassunto" può appartenere a un’altra edizione di questo titolo.

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