- Pandas training. Decision trees.
- Metric methods. KNN.
- Support Vector Machine. Application to TF-IDF. Logistic regression and gradient descent self-implemented. Classification metrics (accuracy, precision, recall, F1 measure).
- Multiparametric linear regression. Categorical features. Principal component analysis.
- Bagging and boosting. Random forest and boosted gradient classification.
- K-Means for reducing number of features