CREATOR ONLY

Recognizing Handwritten Digits with Python

Build a Logistic Regression Model with SKLearn, load and analyze 70,000 digits from the MNIST dataset, and accurately recognize samples!

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Recognizing Handwritten Digits with Python demo

TECHNOLOGIES

Machine Learning
SKLearn

WATCH TIME

32 minutes

LEVEL

All

Overview

This project is a beginner-friendly Python and Machine Learning application focused on building a logistic regression model to analyze and recognize handwritten digits from the MNIST dataset. We’ll read over 70,000 input samples, visualize and analyze each digit, create our testing and training datasets, fit the logistic regression model and visualize the accuracy of our predictions with a confusion matrix!

Project Tasks

Welcome to the project!

2 min

Processing and Visualizing our Digits

Process and visualize your handwritten digits in a Jupyter lab notebook.

7 min

Getting our MNIST Digits Dataset

Use the MNIST dataset to access thousands of handwritten digits to analyze.

8 min

Visualizing our Predictions and Confusion Matrix

Based on your numerical algorithm, predict the value of your visualized digit and assess the accuracy using a Confusion Matrix.

10 min

Applying Logistic Regression

Apply a logistic regression and a numerical algorithm on the visualized digits.

8 min