Most data scientists spend more time working on the data than on the algorithms. Most books and courses on machine learning, however, focus on the algorithms. This book addresses this gap in material about the data side of machine learning.
The first part of this book introduces the building blocks for creating training and evaluation data: annotation, active learning, and the human–computer interaction concepts that help humans and machines combine their intelligence most effectively. By the end of chapter 2, you will have built a human-in-the-loop machine learning application for labeling news headlines, completing the cycle from annotating new data to retraining a model and then using the new model to decide which data should be annotated next.
In the remaining chapters, you will learn how you might extend your first application with more sophisticated techniques for data sampling, annotation, and combining human and machine intelligence. The book also covers how to apply the techniques you will learn to different types of machine learning tasks, including object detection, semantic segmentation, sequence labeling, and language generation.