Intro to Model Optimization in Machine Learning
Delve into machine learning fundamentals using the Wisconsin Breast Cancer Dataset. This course focuses on key ML techniques like data exploration, model tuning, and evaluation. Master hyperparameter tuning, regularization, and ensemble methods through practical exercises to boost your predictive models' accuracy and reliability.
Lessons and practices
Lesson 1: Exploring the Wisconsin Breast Cancer Dataset
Getting Acquainted with the Breast Cancer Dataset
Unveiling the Unique Features in Dataset
Debugging Class Counts in Dataset
Unveiling Descriptive Statistics of Dataset
Lesson 2: Hyperparameter Tuning in Logistic Regressions
Correcting Data Scaling Issues
Tuning Hyperparameters with GridSearchCV
Navigating the Hyperparameter Space
Lesson 3: Optimizing Decision Trees with Hyperparameter Tuning
Widening the GridSearchCV Parameter Range
Sailing Through Decision Tree Hyperparameters
Navigating the GridSearch Space
Exploring the Decision Tree Parameter Space
Lesson 4: Regularization Techniques in Machine Learning: Enhancing Model Generalization
Switching to L2 Regularization in Logistic Regression
Mastering L2 Regularization in Logistic Regression
Cracking the Regularization Code
Mastering L1 Regularization Performance
Applying L1 Regularization Mastery
Lesson 5: Elevating Predictive Models with RandomForest and GradientBoosting Techniques
Adjusting RandomForest Hyperparameters for Better Accuracy
Prepare Data for Your Gradient Boosting Classifier
Hyperparameters and the Art of Boosting
Boost Your Classifier with Gradient Boosting
Lesson 6: Mastering Model Evaluation: Performance Metrics & Selection in Machine Learning
Scaling Features for Improved Accuracy
Performance Boost with Data Scaling
Deploying a Complete Machine Learning Pipeline
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.