Here we will learn :
Model Evaluation & Validation
- Cross-validation techniques: k-fold cross-validation, stratified cross-validation.
- Hyperparameter tuning: grid search, random search, Bayesian optimization.
- Overfitting, underfitting, bias-variance tradeoff.
Curriculum
- 6 Sections
- 29 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Cross-Validation in Machine Learning4
- Hyperparameter tuning15
- 2.1What is hyperparameter tuning?
- 2.2What are hyperparameters?
- 2.3How do you identify hyperparameters?
- 2.4Why is hyperparameter tuning important?
- 2.5How does hyperparameter tuning work?
- 2.6What are the hyperparameter tuning techniques?
- 2.7What are examples of hyperparameters?
- 2.8Hyperparameter tuning – Grid
- 2.9How to Apply GridSearchCV?
- 2.10Cross-Validation and GridSearchCV
- 2.11Hyperparameters vs Parameters
- 2.12Random Search Introduction
- 2.13What is a Randomized Search?
- 2.14Bayesian Optimization
- 2.15Baysian Optimization
- What is Overfitting?1
- underfitting2
- The Challenge of Underfitting and Overfitting in Machine Learning2
- Understanding the Bias-Variance Tradeoff5
