5.0
Intermediate
5h
Mastering Hyperparameter Optimization for Machine Learning
Delve into hyperparameter optimization for machine learning models, exploring techniques like grid search, SMBO, TPE, and genetic algorithms using real-world datasets to enhance model performance.
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 methods for logistic regression and random forest models. Then, you’ll understand sequential model-based optimization (SMBO) and Tree-Structured Parzen Estimator (TPE), applying them to k-nearest neighbors (KNN) and histogram-based gradient boosting algorithms. You’ll finish by understanding and applying genetic algorithms to find the best hyperparameters for the KNN algorithm and random forest model.
After completing this course, you’ll have gained skills to master the hyperparameter optimization.
Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyper...Show More
WHAT YOU'LL LEARN
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
Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization
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TAKEAWAY SKILLS
Content
1.
Introduction
4 Lessons
Get familiar with hyperparameters, their optimization, and the dataset for machine learning models.
2.
Random Search Method
7 Lessons
Grasp the fundamentals of random search for hyperparameter optimization to enhance model performance.
3.
Grid Search Method
6 Lessons
Break apart the Grid Search method's steps, practical applications, and its pros and cons.
4.
Sequential Model-Based Optimization Method
6 Lessons
Apply your skills to optimize hyperparameters efficiently using Sequential Model-Based Optimization (SMBO).
5.
Tree-Structured Parzen Estimators Method
6 Lessons
Explore the Tree-Structured Parzen Estimator method for enhancing hyperparameter optimization in machine learning.
6.
Genetic Algorithm
6 Lessons
Follow the process of using genetic algorithms to optimize hyperparameters for machine learning models.
7.
Conclusion
1 Lessons
Practice using hyperparameter optimization techniques in machine learning projects.
8.
Appendix
1 Lessons
Get familiar with installing Python packages using Anaconda for efficient environment management.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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