Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.
InstructorSoledad GalliInstruction covers diverse approaches including SMAC, Tree-structured Parzen Estimators (TPE), and various open-source libraries like Optuna and Hyperopt.
The curriculum includes specific modules on Bayesian Inference, Joint and Conditional Probabilities, and Gaussian Distributions.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
Course slide listing topics such as Metrics, Cross-Validation Schemes, and Bayesian Optimization alongside a monitor displaying a geometric diagram
Course slide listing four learning objectives for students, including improving ML algorithm performance and using open-source packages.
FreeLessons explain complex techniques in plain English and utilize Jupyter notebooks to assist with practical implementation.
The curriculum relies on provided notebooks rather than formal, guided assignments or interactive coding exercises.
The curriculum aligns with the requirement for a strong machine learning background and specific tool knowledge.
