Solve complex big data challenges by unleashing the power of active machine learning with Python
Key Features
- Learn how to implement a pipeline to get the best model using your large datasets, at lower costs
- Gain to more profound insights within your data, while achieving greater efficiency and speed
- Apply your knowledge to real world use cases and solve complex ML problems
Book Description
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.
What you will learn
- Learn the fundamentals of active machine learning
- Understand query strategies for optimal model training with minimal data
- Discover how to tackle class imbalance, concept drift, and other data challenges
- Evaluate and analyze active learning model performance
- Integrate active learning libraries into workflows effectively
- Optimize workflows for human labellers
- Harness active learning for computer vision and NLP tasks
- Explore the finest active learning tools available today
Who this book is for
This 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 Contents
- Introducing Active Machine Learning
- Designing Query Strategy Frameworks
- Managing the Human in the Loop
- Applying Active Learning to Computer Vision
- Implementing Active Learning for NLP
- Evaluating and Enhancing Efficiency
- Exploring Deep Active Learning
- Tackling Specialized Tasks
- Utilizing Tools and Packages for Active Learning
Margaux Masson-Forsythe is a skilled machine learning engineer and advocate for advancements in surgical data science and climate AI. As the Director of Machine Learning at Surgical Data Science Collective, she builds computer vision models to detect surgical tools in videos and track procedural motions. Masson-Forsythe manages a multidisciplinary team and oversees model implementation, data pipelines, infrastructure, and product delivery. With a background in computer science and expertise in machine learning, computer vision, and geospatial analytics, she has worked on projects related to reforestation, deforestation monitoring, and crop yield prediction.
