Sebastian Raschka is the author of the bestselling book “Python Machine Learning.” As a Ph.D. candidate at Michigan State University, he is developing new computational methods in the field of computational biology. Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning. Sebastian loves to write and talk about data science, machine learning, and Python, and he is really motivated to help people developing data-driven solutions without necessarily requiring a machine learning background.
Sebastian is also actively contributing to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. In his free-time, Sebastian is also working on models for sports predictions, and if he is not sitting in front of a computer, he enjoys playing sports in his spare time.
The main subject we cover in the episode is model evaluation. Since we recorded back in May, Sebastian has put together a great three-part tutorial that goes into more detail than we could get into during the interview.
- Part I: The Basics
- Part II: Bootstrapping and Uncertainties
- Part III: Cross-validation and hyperparameter tuning
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