- Introduction to Machine Learning
- Data Science Pipeline: Frame - Acquire - Refine - Explore - Model - Insight
- Types of ML Problems
- Features and Targets
- ML Thought Process: Regression & Classification
- ML Thought Process (contd.)
- Measurement & Metrics
- Overfitting, Bias & Variance
- Regularization
- Evaluation and Cross Validation
- Hands-on Session: Linear Regression
- Hands-on Session: Logistic Regression
- Simple Trees and Challenges
- Ensembles - Bagging, Patching, Random Subspace
- Random Forest
- Measurement: Variable Importance, OOB
- Gradient Boosting
- Hands-on Session: Trees
- Hands-on Session: Random Forest
- Hands-on Session: Gradient Boosting
- Feature Engineering
- Unbalanced Classes (Advanced)
- Model Pipelines
- Hands-on Session: Pipelines
- Hands-on Session: Pipelines (contd.)
- Practical Guidelines for ML
- Next Steps
- Wrap-up and Feedback
- Office Hours
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- 0900 - 0930: Breakfast
- 1115 - 1130: Tea Break
- 1315 - 1400: Lunch
- 1530 - 1545: Tea Break