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

Margaux Masson-Forsythe

ISBN 10: 1835464947 / ISBN 13: 9781835464946

Verlag: Packt Publishing, 2024

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Use active machine learning with Python to improve the accuracy of predictive models, streamline the data analysis process, and adapt to evolving data trends, fostering innovation and progress across diverse fieldsKey FeaturesLearn how to implement a pipeline for optimal model creation from large datasets and at lower costsGain profound insights within your data while achieving greater efficiency and speedApply your knowledge to real-world use cases and solve complex ML problemsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook DescriptionBuilding 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 learnMaster the fundamentals of active machine learningUnderstand query strategies for optimal model training with minimal dataTackle class imbalance, concept drift, and other data challengesEvaluate and analyze active learning model performanceIntegrate active learning libraries into workflows effectivelyOptimize workflows for human labelersExplore the finest active learning tools available todayWho this book is forIdeal 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.Table of ContentsIntroducing Active Machine LearningDesigning Query Strategy FrameworksManaging the Human in the LoopApplying Active Learning to Computer VisionLeveraging Active Learning for Big DataEvaluating and Enhancing EfficiencyUtilizing Tools and Packages for Active Learning.

Bestandsnummer des Verkäufers 9781835464946

Bibliografische Details

• Titel: Active Machine Learning with Python : Refine and elevate data quality over quantity with active learning
• Verlag: Packt Publishing
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1835464947
• ISBN-13: 9781835464946
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 191 H x 10 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 10 mm
• Gewicht: 341 Gramm

Über diesen Titel

Inhaltsangabe

Solve complex big data challenges by unleashing the power of active machine learning with Python

Key Features

• Learn how to implement a pipeline to get the best model using your large datasets, at lower costs
• Gain to more profound insights within your data, while achieving greater efficiency and speed
• Apply your knowledge to real world use cases and solve complex ML problems

Book Description

Building accurate machine learning models requires quality data - lots of it. For most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. That's where active learning comes in. This hands-on guide shows you how to train robust models with just a fraction of the data using Python's powerful active learning tools.

You'll start by mastering the fundamental techniques of active learning like membership query synthesis, stream-based sampling, and pool-based sampling. See how little data you need to tackle common challenges like class imbalance, concept drift, and more. Then dive into the practice of active learning, constructing query strategies, analyzing model performance, and selecting optimal training sets.

By the end of the book you'll be able to apply active learning to solve real-world problems in sectors from computer vision to natural language processing. Unlock the true potential of your data with Active Machine Learning and Python. Start building better ML with less today.

What you will learn

• Learn the fundamentals of active machine learning
• Understand query strategies for optimal model training with minimal data
• Discover how to 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 labellers
• Harness active learning for computer vision and NLP tasks
• Explore the finest active learning tools available today

Who this book is for

This book is perfect for data scientists and ML engineers who want to maximize model performance while minimizing costly data labelling. Through hands-on examples, you'll learn active learning techniques to train precise models with carefully curated, high-quality datasets - no need for massive volumes of random data. Both technical practitioners and team leads will discover proven methods to slash data requirements and iterate faster. If you want to optimize your ML workflows, this is your guide to the art of quality over quantity.

Table of Contents

1. Introducing Active Machine Learning
2. Designing Query Strategy Frameworks
3. Managing the Human in the Loop
4. Applying Active Learning to Computer Vision
5. Implementing Active Learning for NLP
6. Evaluating and Enhancing Efficiency
7. Exploring Deep Active Learning
8. Tackling Specialized Tasks
9. Utilizing Tools and Packages for Active Learning

Über die Autorin bzw. den Autor

Margaux Masson-Forsythe is a skilled machine learning engineer and advocate for advancements in surgical data science and climate AI. As the Director of Machine Learning at Surgical Data Science Collective, she builds computer vision models to detect surgical tools in videos and track procedural motions. Masson-Forsythe manages a multidisciplinary team and oversees model implementation, data pipelines, infrastructure, and product delivery. With a background in computer science and expertise in machine learning, computer vision, and geospatial analytics, she has worked on projects related to reforestation, deforestation monitoring, and crop yield prediction.