Building 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.

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Spis treści

Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning eBook -- spis treści

• 1. Introducing Active Machine Learning
• 2. Designing Query Strategy Frameworks
• 3. Managing the Human in the Loop
• 4. Applying Active Learning to Computer Vision
• 5. Leveraging Active Learning for Big Data
• 6. Evaluating and Enhancing Efficiency
• 7. Utilizing Tools and Packages for Active Learning