[UDM] Automated Machine Learning – AutoML, TPOT, H2O, AutoKeras Course (Mar 2025) + Resources

Discover the Potential of Automated Machine Learning
Enter the world of the developing field of AutoML with the Automated Machine Learning – AutoML, TPOT, H2O, AutoKeras Course. Made to be accessible to Python-wielding users who already have a fundamental knowledge of machine learning, it will teach you the most advanced tools, such as TPOT, H2O AutoML and AutoKeras. You will find out how to automate tedious work: model selection, feature engineering, and hyperparameter tuning, becoming up to 10x more productive and improving model performance.

Gain Knowledge in projects in the real world.
By the end of the theory, there will be five practical projects, demonstrating AutoML in practice. As wide-ranging as recognizing credit card fraud via AutoML to identifying handwritten digits with AutoKeras, both projects illustrate how easy it is to complete the pipeline given the accuracy. Such practical examples are likely to make you learn the logic of every automation tool, which is to prepare you before facing real machine learning problems.

Learn how to use Master Top AutoML Tools: TPOT and H2O
Get deeper into AutoML ecosystem with TPOT and H2O. TPOT has a genetic programming methodology that will assist you in creating optimized machine learning pipelines on insurance data, and H2O AutoML will determine customer churn and future sales. With these tools, you can easily compare and analyze each of your models in a short time basis, thanks to them, you can make intelligent decisions with a minimum manual input.

Enhance your effectiveness and your competencies
Whether you are new to automation and want to explore how you can automate your processes or are an experienced data practitioner interested in making your modeling more productive, the Automated Machine Learning – AutoML, TPOT, H2O, AutoKeras Course empowers you with the hands-on skills that can achieve both. A final goal is that you will be sure to use AutoML methods on both classification and regression problems, and when to trust one method over another because of automation or manual tuning.