- Data Collection, Cleaning, Bias in the Data
- k-Nearest Neighbor (kNN), Linear Regression, Interpretations
- Multi & Poly Linear Regression
- Model Selection & Cross Validation
- Hypothesis Testing
- Regularization: Ridge & Lasso
- Logistic Regression, Interpretations
- Multi-Class Logistic Regression
- Data Summarization & Submodularity
- Submodular Maximization
- Model Interpretation
- Learning from Large Datasets
Python, Jupyter, Numpy, Pandas, Matplotlib, Plotly, Sklearn