Regression is one of the most widely used modeling techniques and is much beloved by everyone ranging from business professionals to data scientists. Using scikit-learn, you can easily implement virtually every important type of regression with ease. In this course, Building Regression Models with scikit-learn, you will gain the ability to enumerate the different types of regression algorithms and correctly implement them in scikit-learn. First, you will learn what regression seeks to achieve, and how the ubiquitous Ordinary Least Squares algorithm works under the hood. Next, you will discover how to implement other techniques that mitigate overfittings such as Lasso, Ridge and Elastic Net regression. You will then understand other more advanced forms of regression, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent. Finally, you will round out the course by understanding the hyperparameters that these various regression models possess, and how these can be optimized. When you are finished with this course, you will have the skills and knowledge to select the correct regression algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Understanding Linear Regression as a Machine Learning Problem
  42mins
• Building a Simple Linear Model
  29mins
• Building Regularized Regression Models
  35mins
• Performing Regression Using Multiple Techniques
  36mins
• Hyperparameter Tuning for Regression Models
  16mins