This module introduces key query strategy frameworks used in active machine learning, including uncertainty sampling, query-by-committee, EMC, EER, and density-weighted methods. Learners will discover how these strategies prioritize data selection to improve model performance and efficiency. Practical insights into measuring uncertainty and optimizing labeling efforts are provided.

This module explores the essential components of integrating human input into active machine learning workflows. Learners will discover how to design effective labeling interfaces, utilize leading annotation tools, and implement strategies to ensure data quality and balanced datasets. Practical scenarios highlight the importance of managing human error and maintaining high annotation standards.

This module introduces active learning strategies for computer vision, focusing on reducing labeling effort through uncertainty sampling. Learners will implement and evaluate active learning workflows for image classification, object detection, and instance segmentation tasks using convolutional neural networks and modern tools.

This module introduces strategies for efficiently handling large-scale data using active learning techniques and the Lightly tool. Learners will discover how to select the most informative frames to optimize labeling efforts and improve machine learning model accuracy. Additional topics include scheduling active learning runs and leveraging self-supervised learning (SSL) for enhanced data representation.

This module guides learners through strategies for assessing and improving the efficiency of active machine learning systems. You will explore automation, monitoring, and stopping criteria, as well as techniques for detecting data drift and model decay in production environments. By the end, you'll be equipped to optimize and maintain high-performing ML pipelines.

This module introduces key Python libraries and frameworks for implementing active machine learning, such as scikit-learn and modAL. Learners will explore practical workflows and compare popular tools and labeling platforms to enhance model development efficiency. By the end, you'll be equipped to select and utilize the most suitable resources for your active ML projects.