Building Regression Models with scikit-learn

This course covers important techniques such as ordinary least squares regression, moving on to lasso, ridge, and Elastic Net, and advanced techniques such as Support Vector Regression and Stochastic Gradient Descent Regression.
Course info
Level
Intermediate
Updated
Jun 28, 2019
Duration
2h 42m
Table of contents
Course Overview
Understanding Linear Regression as a Machine Learning Problem
Building a Simple Linear Model
Building Regularized Regression Models
Performing Regression Using Multiple Techniques
Hyperparameter Tuning for Regression Models
Description
Course info
Level
Intermediate
Updated
Jun 28, 2019
Duration
2h 42m
Description

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.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi. My name is Janani Ravi, and welcome to this course on Building Regression Models with scikit-learn. A little about myself. I have a master's degree in electrical engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. Regression is one of the most widely used modeling techniques, and it's 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, you will gain the ability to enumerate in 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 overfitting, 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're finished with this course, you will have the skills and knowledge to select the correct regression algorithm based on the problem you're trying to solve and also implement it correctly using scikit-learn.