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Active Machine Learning with Python

Active Machine Learning with Python: Refine and elevate data quality over quantity with active learning

By Margaux Masson-Forsythe
$35.99 $24.99
Book Mar 2024 176 pages 1st Edition
eBook
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Print
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eBook
$35.99 $24.99
Print
$44.99
Subscription
$15.99 Monthly

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Product Details


Publication date : Mar 29, 2024
Length 176 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781835464946
Category :
toc View table of contents toc Preview Book toc Download Code

Key benefits

  • Learn how to implement a pipeline for optimal model creation from large datasets and at lower costs
  • Gain profound insights within your data while achieving greater efficiency and speed
  • Apply your knowledge to real-world use cases and solve complex ML problems
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Building accurate machine learning models requires quality data—lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You’ll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you’ll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You’ll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you’ll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools.

What you will learn

Master the fundamentals of active machine learning Understand query strategies for optimal model training with minimal data Tackle class imbalance, concept drift, and other data challenges Evaluate and analyze active learning model performance Integrate active learning libraries into workflows effectively Optimize workflows for human labelers Explore the finest active learning tools available today

What do you get with eBook?

Feature icon Instant access to your Digital eBook purchase
Feature icon Download this book in EPUB and PDF formats
Feature icon Access this title in our online reader with advanced features
Feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : Mar 29, 2024
Length 176 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781835464946
Category :

Table of Contents

13 Chapters
Preface Packt Packt
Part 1: Fundamentals of Active Machine Learning Packt Packt
Chapter 1: Introducing Active Machine Learning Packt Packt
Chapter 2: Designing Query Strategy Frameworks Packt Packt
Chapter 3: Managing the Human in the Loop Packt Packt
Part 2: Active Machine Learning in Practice Packt Packt
Chapter 4: Applying Active Learning to Computer Vision Packt Packt
Chapter 5: Leveraging Active Learning for Big Data Packt Packt
Part 3: Applying Active Machine Learning to Real-World Projects Packt Packt
Chapter 6: Evaluating and Enhancing Efficiency Packt Packt
Chapter 7: Utilizing Tools and Packages for Active ML Packt Packt
Index Packt Packt
Other Books You May Enjoy Packt Packt

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