К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Active Machine Learning with Python · LearnSpace
Назад в каталог
courseraАнализ данных

Active Machine Learning with Python

Курс от Packt
Средний≈ 5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Active machine learning is transforming how organizations build accurate AI systems while reducing the need for large labeled datasets. This course explores the core principles, strategies, and tools used to create efficient machine learning workflows with Python, helping professionals improve model quality while minimizing annotation effort and operational costs. Through practical guidance and hands-on examples, learners will discover how to design query strategies, manage human-in-the-loop systems, and evaluate model efficiency in data-scarce environments. The course also demonstrates how active learning can be applied to computer vision and big data challenges to improve scalability and productivity. Unlike traditional machine learning resources, this course combines foundational theory with implementation-focused techniques that can be applied directly to real-world ML projects. Readers will work with practical workflows, modern Python tools, and efficiency-driven strategies used in production environments. This course is ideal for data scientists, machine learning engineers, and AI practitioners seeking to optimize model training with limited labeled data. A basic understanding of Python programming and machine learning concepts is recommended.

Навыки, которые вы освоите

Applied Machine LearningScikit Learn (Machine Learning Library)Python ProgrammingMachine Learning MethodsModel OptimizationConvolutional Neural NetworksModel TrainingBig DataMLOps (Machine Learning Operations)Machine LearningModel EvaluationComputer VisionData QualityData CollectionSampling (Statistics)Continuous MonitoringImage Analysis

Программа курса

7 модулей · 45 учебных материалов

01Introducing Active Machine Learning6 материалов

Smart Data Selection: Unlocking Efficiency with Active Learning

OverviewВидеоIntroductionЧтениеKey Components of Active Machine Learning SystemsЧтениеStream-based Selective SamplingЧтение

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Active Machine Learning with Python
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 5 ч

7 модулей

Язык: Английский

Часть программы вашего университета
Comparing Active and Passive LearningЧтение
Foundations of Active Machine LearningЗадание
02Designing Query Strategy Frameworks7 материалов

Mastering Active Learning: Strategies for Smart Data Selection

OverviewВидеоIntroductionЧтениеUnderstanding Query-by-Committee ApproachesЧтениеAverage KL DivergenceЧтениеLabeling with EMC SamplingЧтениеSampling with EERЧтениеQuery Strategy Frameworks in Machine LearningЗадание
03Managing the Human in the Loop6 материалов

Optimizing Human Collaboration in Active Machine Learning

OverviewВидеоIntroductionЧтениеExploring Human-in-the-Loop Labeling ToolsЧтениеManual Review of ConflictsЧтениеEnsuring Annotation Quality and Dataset BalanceЧтениеHuman-in-the-Loop and Data Labeling FundamentalsЗадание
04Applying Active Learning to Computer Vision8 материалов

Optimizing Computer Vision with Active Learning Techniques

OverviewВидеоIntroductionЧтениеBuilding a CNN for the CIFAR DatasetЧтениеApplying Uncertainty Sampling to Improve Classification PerformanceЧтениеApplying Active ML to an Object Detection ProjectЧтениеImplementing an Active ML StrategyЧтениеUsing Active ML for a Segmentation ProjectЧтениеActive Learning and Computer Vision ConceptsЗадание
05Leveraging Active Learning for Big Data6 материалов

Optimizing Video Data Labeling with Active Learning and Lightly

OverviewВидеоIntroductionЧтениеSelecting the Most Informative Frames with LightlyЧтениеSchedule the Active ML RunЧтениеSSL with Active MLЧтениеActive Learning and Self-Supervised Techniques in Big DataЗадание
06Evaluating and Enhancing Efficiency7 материалов

Optimizing Active ML Pipelines: Automation, Monitoring, and Early Detection

OverviewВидеоIntroductionЧтениеMonitoring Active ML PipelinesЧтениеDetermining When to Stop Active ML RunsЧтениеActive ML to Monitor Models in ProductionЧтениеEarly Detection for Data Drift and Model DecayЧтениеEvaluating and Enhancing Efficiency in ML SystemsЗадание
07Utilizing Tools and Packages for Active Learning5 материалов

Navigating the Active Learning Toolkit: From Libraries to Labeling Platforms

OverviewВидеоIntroductionЧтениеModALЧтениеGetting Familiar with the Active ML ToolsЧтениеActive Learning and Tool UtilizationЗадание