Logistic Regression from Scratch

Challenging

107 completions

~ 30 hours

4.2

Come to know logistic regression. Learn the math behind it and implement a solution that will be on par with the one from sklearn. Study two kinds of cost function — Mean Squared Error and Log Loss. These concepts will give you the freedom to combine different tools for solving complex problems.

Provided by

JetBrains Academy

About

In this project, we will take a look at the logistic regression algorithm and build a custom binary classifier. The main idea of the project is to implement gradient descent for two different cost functions, devise a method to predict the probability that a given sample belongs to a certain class, and analyze training errors. And last but not least — put your models to the test on a real dataset.

Graduate project

This project covers the core topics of the Coding Machine Learning Algorithms course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Predict probabilities with a sigmoid function.

Fit a method when the cost function is the Mean squared error.

Build a fit method with a Log-loss cost function.

Make the final touches on the algorithm and visualize the optimization process.

Reviews

Aneurin Sutton

12 months ago

3.7

This is a good project to see how to experiment with and develop your own analysis/learning models and associated cost functions.

Janek Kupper

2 years ago

3.7

I have learned a lot of math and about logistic regression. It was a though project for me.

Vadim Baum

2 years ago

4.0

I learned the difference between MSE and log loss in the logistic regression.

4.2

Learners who completed this project within the Coding Machine Learning Algorithms course rated it as follows:

Usefulness

4.6

Fun

4.2

Clarity

3.8