9781835462683_

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. Thats where active learning comes in. This hands-on guide shows you how to train robust models with just a fraction of the data using Pythons powerful active learning tools. Youll 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 youll 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, youll 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.

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Active Machine Learning with Python

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