Philippe ROSSIGNOL

Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed continuous-valued response.

• This course covers the major topics such as:
  Simple Linear Regression.
  Multiple Regression.
  Assessing Performance.
  Ridge Regression.
  Feature Selection & Lasso.
  Nearest Neighbors & Kernel Regression.
  

• This course teaches how to:
  Describe the input and output of a regression model.
  Compare and contrast bias and variance when modeling data.
  Estimate model parameters using optimization algorithms.
  Tune parameters with cross validation.
  Analyze the performance of the model.
  Describe the notion of sparsity and how LASSO leads to sparse solutions.
  Deploy methods to select between models.
  Exploit the model to form predictions.
  Build regression models to make predictions.
  Implement these techniques in Python.