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.

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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.

Mastering Hyperparameter Optimization for Machine Learning

- **Simplicity:** The random search method is a simple and straightforward method for hyperparameter optimization, as it does not require any knowledge about the structure of the optimization landscape or the relationships between different hyperparameters.








Learn about the advantages and disadvantages of using random search method for hyperparameter optimization.

Introduction

Random Search Method

Grid Search Method

Sequential Model-Based Optimization Method

Tree-Structured Parzen Estimators Method

Genetic Algorithm

Evaluate Hyperparameter Optimization Concepts

Optimizing ML Model for Promotion Selection

Conclusion

Appendix

Predict Cancer Using Machine Learning Models

Advantages and Disadvantages of Random Search Method