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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

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Profile Icon Margaux Masson-Forsythe
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback Mar 2024 176 pages 1st Edition
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Arrow left icon
Profile Icon Margaux Masson-Forsythe
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$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback Mar 2024 176 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$44.99
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Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
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Renews at $19.99p/m

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Active Machine Learning with Python

Introducing Active Machine Learning

Machine learning models require large, labeled datasets, which can be expensive and time-consuming to obtain. Active machine learning (active ML) minimizes the labeling effort needed by intelligently choosing which data points a human should label. In this book, you will gain the necessary knowledge to understand active learning, including its mechanisms and applications. With these fundamentals, the subsequent chapters will equip you with concrete skills to implement active learning techniques on your own.

By the end of this book, you will have practical experience with state-of-the-art strategies to minimize labeling costs and maximize model performance. You will be able to apply active learning to enhance the efficiency and adaptability of your models across different application areas, such as vision and language.

To begin with, this chapter provides an introduction to active ML and explains how it can improve model accuracy using fewer...

Understanding active machine learning systems

Active machine learning (active ML) is a powerful approach that seeks to create predictive models with remarkable accuracy, all while minimizing the number of labeled training examples required. This is achieved by employing a clever strategy that involves selectively choosing the most informative data points to be labeled by a knowledgeable oracle, such as a human annotator. By doing so, active learning enables models to extract the necessary knowledge they need from a relatively small amount of data.

Now, let’s explore some definitions and the fundamental concepts that form the foundation of active ML.

Definition

Active learning can be defined as a dynamic and iterative approach to machine learning, where the algorithm intelligently engages with an oracle to label new data points. An oracle is a source that provides labels for data points queried by the active learner. The oracle acts as a teacher, guiding the model by...

Exploring query strategies scenarios

Active learning can be implemented in different ways, depending on the nature of the unlabeled data and how the queries are performed. There are three main scenarios to consider when implementing active learning:

  • Membership query synthesis
  • Stream-based selective sampling
  • Pool-based sampling

These scenarios offer different ways to optimize and improve the active learning process. Understanding these scenarios can help you make informed decisions and choose the most suitable approach for your specific needs. In this section, we will explore each of these scenarios.

Membership query synthesis

In membership query synthesis, the active learner has the ability to create its own unlabeled data points in order to improve its training. This is done by generating new data points from scratch and then requesting the oracle for labels, as depicted in Figure 1.2. By incorporating these newly labeled data points into its training...

Comparing active and passive learning

In traditional passive machine learning, models are trained on fixed and pre-existing labeled datasets, which are carefully assembled to include both data points and their respective ground truth labels. The model then goes through the dataset once, without any iteration or interaction, and learns the patterns and relationships between the features and labels. This is the passive learning approach. It’s important to note that the model only trains on the finite data it is provided and cannot actively seek out new information or modify its training based on new inputs. Moreover, the labeled datasets required for a passive learning approach come at a cost.

There are several reasons why labeling is expensive in traditional machine learning:

  • Manual labeling requires experts: Accurately labeling data often demands the expertise of domain specialists such as doctors or ecologists. However, their time is limited and valuable, making...

Summary

In this introductory chapter, we covered the fundamentals of active ML and how it contrasts with passive learning approaches.

You learned what active learning is and its goal of maximizing predictive performance with fewer labeled training examples. We discussed the core components of an active learning system: the unlabeled data pool, query strategy, machine learning model, and the oracle labeler.

You now understand the difference between membership query synthesis, stream-based sampling, and pool-based sampling scenarios. We compared active and passive learning, highlighting the benefits of an interactive, iterative approach in active learning.

Importantly, you now know that active learning can produce models with equal or greater accuracy while requiring far less labeled training data. This is critical for reducing the costs of modeling, as labeling is often the most expensive component.

The skills you gained in this introduction will equip you to determine when...

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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.

Who is this book for?

Ideal for data scientists and ML engineers aiming to maximize model performance while minimizing costly data labeling, this book is your guide to optimizing ML workflows and prioritizing quality over quantity. Whether you’re a technical practitioner or team lead, you’ll benefit from the proven methods presented in this book to slash data requirements and iterate faster. Basic Python proficiency and familiarity with machine learning concepts such as datasets and convolutional neural networks is all you need to get started.

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

Product Details

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

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

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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 50%
1 star 0%
Steven Fernandes May 05, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an essential resource for anyone looking to master active machine learning. It provides a clear foundation in the fundamentals and delves into practical query strategies to train models optimally with minimal data. The book effectively addresses common issues such as class imbalance and concept drift, and offers robust methods for evaluating and analyzing the performance of active learning models. Furthermore, it guides readers on how to integrate active learning libraries into existing workflows and optimize these workflows for human labelers. A standout feature is its exploration of the best active learning tools currently available, making it a practical guide for both novices and experienced practitioners in the field.
Amazon Verified review Amazon
David Finlayson May 01, 2024
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
If you have never read anything about Active ML, this book will introduce the concept and show some examples of how to implement the training strategy in Python. The book is very short, only 150 pages of content and expensive for what it is. I feel like you would be better off reading Wikipedia and blog posts dedicated to Active ML.
Amazon Verified review Amazon
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