Machine learning 1
Lab 1. Introduction to machine learning
Lab 2. Initial data analysis, data preparation
Lab 3. Initial feature selection methods
Lab 4. Sample parametric methods: linear regression, logistic regression
practical part for binary logit
presentation for multinomial logit
practical part for multinomial logit
Lab 5. Cost functions and evaluation metrics
Lab 6. Cross-validation methods
Lab 7. K nearest neighbours
Lab 8. Support Vector Machines and Support Vector Regression
Lab 9. Regularization: ridge, LASSO, elastic net
Lab 10. Feature engineering, transformation methods for inputs and target
Lab 11. Rebalancing methods: up-sampling, down-sampling, SMOTE and ROSE
Lab 12. Linear and quadratic discriminant analysis
Lab 13. Workshops - project related consultations (OPTIONAL)
Lab 14. Missing data - checking and simple imputation methods
Lab 15. STUDENT'S PRESENTATIONS
Links
The Comprehensive R Archive Network
R-bloggers - R news and tutorials
In-depth introduction to machine learning in 15 hours of expert videos
An Introduction to Statistical Learning with Applications in R
