Regression

In this section you will work with regression models in order to solve the specified tasks. Please move one to the lessons.

Assignments:

• Linear regression and core concepts of machine learning
  • Basics of linear regression and loss functions
  • Transferring and processing of data
  • Train/Test split and model validation
  • Description of assignment
• Polynomial regression
  • Key concepts of polynomial regression
  • Description of assignment
• l1 and l2 regularization
  • Overfitting problem
  • Underfitting problem
  • Regularization
  • Feature normalization
  • Description of assignment
• SVM (Support Vector Machines)
  • The gist of SVM
  • Handling Overfitting
  • Description of assignment
• Decision trees family
  • Decision trees
  • Random forest
  • GBDT (Gradient Boosted Decision Trees)
  • Description of assignment