Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
I've learned a lot of new things about Machine Learning I would never even have considered before.
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For me this book is an eye opening for a new topic: how is possible to improve the results of my ML application in the context of the actual evolution of these technologies?
I am very excited that this book exists. It's unlike any other book I've seen on the subject and addresses some very important topics.
The author does an excellent job describing the techniques and the pros and cons of every presented approach.