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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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Steven Fernandes May 05, 2024

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

David Finlayson May 01, 2024

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.

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

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