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Active Machine Learning with Python
Active Machine Learning with Python

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

By Margaux Masson-Forsythe
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Book Mar 2024 176 pages 1st Edition
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Product Details


Publication date : Mar 29, 2024
Length 176 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781835464946
Category :
Table of content icon View table of contents Preview book icon Preview Book

Active Machine Learning with Python

Part 1: Fundamentals of Active Machine Learning

In the rapidly evolving landscape of machine learning (ML), the concept of active ML has emerged as a transformative approach that optimizes the learning process by selectively querying the most informative data points from unlabeled datasets. This part of the book is dedicated to laying the foundational principles, strategies such as uncertainty sampling, query-by-committee, expected model change, expected error reduction, and density-weighted methods, and considerations essential for understanding and implementing active ML effectively. Through a structured exploration, we aim to equip readers with a solid grounding of the best practices for managing the human in the loop by exploring labeling interface design, effective workflows, strategies for handling model-label disagreements, finding adequate labelers, and managing them efficiently.

This part includes the following chapters:

  • Chapter 1, Introducing Active Machine Learning
  • Chapter 2, Designing Query Strategy Frameworks
  • Chapter 3, Managing the Human in the Loop
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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

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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 Chevron down icon Chevron up icon
1. Part 1: Fundamentals of Active Machine Learning Chevron down icon Chevron up icon
2. Chapter 1: Introducing Active Machine Learning Chevron down icon Chevron up icon
3. Chapter 2: Designing Query Strategy Frameworks Chevron down icon Chevron up icon
4. Chapter 3: Managing the Human in the Loop Chevron down icon Chevron up icon
5. Part 2: Active Machine Learning in Practice Chevron down icon Chevron up icon
6. Chapter 4: Applying Active Learning to Computer Vision Chevron down icon Chevron up icon
7. Chapter 5: Leveraging Active Learning for Big Data Chevron down icon Chevron up icon
8. Part 3: Applying Active Machine Learning to Real-World Projects Chevron down icon Chevron up icon
9. Chapter 6: Evaluating and Enhancing Efficiency Chevron down icon Chevron up icon
10. Chapter 7: Utilizing Tools and Packages for Active ML Chevron down icon Chevron up icon
11. Index Chevron down icon Chevron up icon
12. Other Books You May Enjoy Chevron down icon Chevron up icon

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