Active Machine Leaing with Python:Refine and elevate data quality over quantity with active leaing

by: Margaux Masson-Forsythe (Author)

Publisher: Packt Publishing - ebooks Account

Publication Date: 9 April 2024

Language: English

Print Length: 250 pages

ISBN-10: 1835464947

ISBN-13: 9781835464946

Book Description

Use active machine leaing 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 FeaturesLea 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 eBookBook DescriptionBuilding accurate machine leaing 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 leaing demonstrates how to train robust models with just a fraction of the data using Python's powerful active leaing tools.You'll master the fundamental techniques of active leaing, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active leaing algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine leaing techniques, you'll lea 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 leaing systems through performance evaluation.By the end of the book, you'll be able to enhance your active leaing projects by leveraging Python libraries, frameworks, and commonly used tools.What you will leaMaster the fundamentals of active machine leaingUnderstand query strategies for optimal model training with minimal dataTackle class imbalance, concept drift, and other data challengesEvaluate and analyze active leaing model performanceIntegrate active leaing libraries into workflows effectivelyOptimize workflows for human labelersExplore the finest active leaing 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 leaing concepts such as datasets and convolutional neural networks is all you need to get started.Table of ContentsIntroducing Active Machine LeaingDesigning Query Strategy FrameworksManaging the Human in the LoopApplying Active Leaing to Computer VisionLeveraging Active Leaing for Big DataEvaluating and Enhancing EfficiencyUtilizing Tools and Packages for Active Leaing
About the Author
Use active machine leaing 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 FeaturesLea 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 eBookBook DescriptionBuilding accurate machine leaing 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 leaing demonstrates how to train robust models with just a fraction of the data using Python's powerful active leaing tools.You'll master the fundamental techniques of active leaing, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active leaing algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine leaing techniques, you'll lea 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 leaing systems through performance evaluation.By the end of the book, you'll be able to enhance your active leaing projects by leveraging Python libraries, frameworks, and commonly used tools.What you will leaMaster the fundamentals of active machine leaingUnderstand query strategies for optimal model training with minimal dataTackle class imbalance, concept drift, and other data challengesEvaluate and analyze active leaing model performanceIntegrate active leaing libraries into workflows effectivelyOptimize workflows for human labelersExplore the finest active leaing 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 leaing concepts such as datasets and convolutional neural networks is all you need to get started.Table of ContentsIntroducing Active Machine LeaingDesigning Query Strategy FrameworksManaging the Human in the LoopApplying Active Leaing to Computer VisionLeveraging Active Leaing for Big DataEvaluating and Enhancing EfficiencyUtilizing Tools and Packages for Active Leaing