Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis
Apr 07, 2016
This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.
Jan 02, 2017
This course is great. Things are very clearly explained. I am particularly happy because it helped me to understand many mathematical concepts. I will try not to be scared about formulas anymore.
Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".<p> In this module, we describe the high-level regression task and then specialize these concepts to the simple linear regression case. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Based on this fitted function, you will interpret the estimated model parameters and form predictions. You will also analyze the sensitivity of your fit to outlying observations.<p> You will examine all of these concepts in the context of a case study of predicting house prices from the square feet of the house.