Linear Regression in scikit-learn

Intermediate
27m 23s
332
4.5/5

This lesson is the second in a two-part series that covers how to build machine learning pipelines using scikit-learn, a library for the Python programming language. This is a hands-on lesson containing demonstrations that you can follow along with to build your own machine learning models.

Learning Objectives

  • Explore supervised-learning techniques used to train a model in scikit-learn by using a simple regression model
  • Understand the concept of the bias-variance trade-off and regularized ML models
  • Explore linear models for classification and how to evaluate them 
  • Learn how to choose a model and fit that model to a dataset

Intended Audience

This lesson is intended for anyone interested in machine learning with Python.

Prerequisites

To get the most out of this lesson, you should have first taken Part One of this two-part series.

Resources

The resources related to this lesson can be found in the following GitHub repo: https://github.com/cloudacademy/ca-machine-learning-with-scikit-learn

About the Author
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Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.

He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.

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