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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 eBookBook 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.