Apply advanced techniques for optimizing Machine Learning processes. Bayesian optimization helps pinpoint the best configuration for your Machine Learning models with speed and accuracy. Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, AB testing, and other aspects of the Machine Learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t have to be difficult! You’ll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting edge Python libraries. The book’s easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian optimization helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and Machine Learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you’re tuning machine learning models, recommending products to customers, or engaging in research, Bayesian optimization can help you make better decisions, faster. For Machine Learning practitioners who are confident in math and statistics.