Project

Classification of Handwritten Digits

Challenging

197 completions

~ 32 hours

4.5

Get hands-on experience with the Keras dataset, train a variety of classification algorithms, and find the best one using scikit-learn tools.

Provided by

JetBrains Academy

About

In this project, you are going to explore the main classification algorithms and learn how to find and train the best possible model for the classification of handwritten digits. You will need to process a dataset that includes images of handwritten numbers from 0 to 9. The ultimate goal is to train the model to identify a digit on the picture. Sounds interesting? Let's dive into it!

Graduate project

This project covers the core topics of the Introduction to Data Science 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:

Figure out how to load the dataset from the external resource, preprocess arrays, and provide general information about the data.

Split the data into train and test sets and verify the class distribution.

Explore sklearn classification algorithms, train the models, and compare the results.

Normalize data, train the models, and explore if that improves the results.

Search through classifier parameter values and find a set of parameters that yields the best result.

Reviews

Aidan Keith Naidoo

7 months ago

4.7

wheeew my laptop almost broke with this one :) but what a great project i enjoyed it a lot was cool seeing my laptop use it resources to get the outputs but i learned what you must do to really narrow down the right model for what you trying to do. I still have much more to understand but its a real ...

Michał Głomski

12 months ago

5.0

Very cool overview of machine learning and data preprocessing, keep it up

Krzysztof Kopel

1 year ago

4.0

For me, this project was a fitting conclusion of the whole course. I learned to compare several ML models, use normalization and hyperparameter tuning to achieve better accuracy.

4.5

Learners who completed this project within the Introduction to Data Science course rated it as follows:

Usefulness

4.8

Fun

4.5

Clarity

4.2