Solve complex big data challenges by unleashing the power of active machine learning with PythonKey FeaturesLearn how to implement a pipeline to get the best model using your large datasets, at lower costsGain to more profound insights within your data, while achieving greater efficiency and speedApply your knowledge to real world use cases and solve complex ML problemsBook DescriptionBuilding 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 learnLearn the fundamentals of active machine learningUnderstand query strategies for optimal model training with minimal dataDiscover how to tackle class imbalance, concept drift, and other data challengesEvaluate and analyze active learning model performanceIntegrate active learning libraries into workflows effectivelyOptimize workflows for human labellersHarness active learning for computer vision and NLP tasksExplore the finest active learning tools available todayWho this book is forThis 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 ContentsIntroducing Active Machine LearningDesigning Query Strategy FrameworksManaging the Human in the LoopApplying Active Learning to Computer VisionImplementing Active Learning for NLPEvaluating and Enhancing EfficiencyExploring Deep Active LearningTackling Specialized TasksUtilizing Tools and Packages for Active Learning. .