Toggle navigation sidebar
Toggle in-page Table of Contents
BAIT 509
Business Applications of Machine Learning
Things You Should Know
Who: Quan Nguyen
How: The Course Structure
What: Learning Outcomes
Lectures
1. Intro to ML & Decision Trees
2. Splitting and Cross-validation
3. Baseline models & k-Nearest Neighbours
4. kNN regression, Support Vector Machines, and Feature Preprocessing
5. Preprocessing Categorical Features and Column Transformer
6. Naive Bayes and Hyperparameter Optimization
7. Linear Models
8. Business Objectives/Statistical Questions and Feature Selection
9. Classification and Regression Metrics
10. Data Science Ethics
11. Multi-Class Classification (Optional)
References
Attribution
Index
