Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.
If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how.
We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.
Specifically, you will learn:
- What hyperparameters are and why tuning matters
- The use of cross-validation and nested cross-validation for optimization
- Grid search and Random search for hyperparameters
- Bayesian Optimization
- Tree-structured Parzen estimators
- SMAC, Population Based Optimization and other SMBO algorithms
- How to implement these techniques with available open source packages including Hyperopt, Optuna, Scikit-optimize, Keras Turner and others.
By the end of the course, you will be able to decide which approach you would like to follow and carry it out with available open-source libraries.
This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.