Fundamentals of machine learning 

offered by HSE University

Define object, target variable, model, loss function

Identify problems of classification, regression, clustering

Use the pandas library to work with tabular data

Formulate k nearest neighbors model for classification and regression

Choose the right metric for the task

Assess the generalization ability of a model using cross-validation

Linear regression model

Linear regression training

Using regularization to reduce the likelihood of overfitting

Know the Gradient Descent Algorithm

Train a differentiable model with a differentiable loss function using gradient descent

Select gradient descent parameters for a specific task

Linear classification model

Classification quality metrics for a specific task

Upper Bounds on the Threshold Loss Function for Gradient Learning of Linear Classifiers

Know the logistic regression model

Know the support vector machine

Solve text classification problems

Know the decision tree model

Train decision trees with a greedy algorithm

The main methods of building compositions: bagging, stacking, blending

Train random forest

Calculate Feature Importance in Decision Tree Compositions