Intermediate
37 Lessons
5h
Certificate of Completion
Takeaway Skills
Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization
Hands-on experience configuring, implementing, and evaluating hyperparameter optimization techniques using Python
Understanding the advantages and disadvantages of the various hyperparameter optimization methods
Working knowledge of Python libraries such as scikit-learn, TPOT, scikit-optimize, and Optuna for hyperparameter optimization
Course Overview
Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyperparameters can enhance the performance of most machine learning models. This course will equip you with the skills to optimize hyperparameters for various machine learning models. You’ll begin with the introduction of hyperparameters and understand the need for optimizing them. Using a loan approval dataset for binary classification, you’ll explore both random and grid search met...Show More
Course Content
Introduction
Random Search Method
Grid Search Method
Sequential Model-Based Optimization Method
Tree-Structured Parzen Estimators Method
Genetic Algorithm
6 Lessons
Evaluate Hyperparameter Optimization Concepts
Assessment
Optimizing ML Model for Promotion Selection
Project
Conclusion
1 Lesson
Appendix
1 Lesson
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