Linear Regression with Python
INTERMEDIATE
#python
Author: Volodymyr Romanovych
Course description
Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.
Complete all chapters to get certificate
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Simple Linear Regression
Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.
What Is The Linear Regression
Finding The Parameters
Building The Linear Regression Using NumPy
Building The Linear Regression Using Statsmodels
Multiple Linear Regression
Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.
Challenge
Linear Regression With Two Features
Linear Regression With n Features
Building Multiple Linear Regression
Choosing The Features
Polynomial Regression
A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.
Challenge
Quadratic Regression
Polynomial Regression
Building The Polynomial Regression
Interpolation vs Extrapolation
Choosing The Best Model
Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.
Challenge 1
Metrics
Overfitting
R-squared
Challenge 2