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Hands-on Traffic Sign Image Classification for Self-Driving Cars using Deep Learning (Convolutional Neural Network)
Updated on Sep, 2026
Programming, Data Science, Deep Learning
Training 5 or more people ?
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This is a Hands-on Project. You learn by Practice.
No unnecessary lectures. No unnecessary details.
A precise, to-the-point and efficient course made for those who want to learn the most important part of Data Science: Importing Datasets, Building Models using the Datasets and Training and Testing the Models. Everything else revolves around this.
However, for the sake of this project, we will use traffic signs for autonomous vehicles to learn about Deep Learning and Data Science. The same process can be repeated for other projects too. The same process and techniques can be repeated for other Deep learning projects. Some such projects that you can build following similar process are:
Self-Driving Cars (This project)
Skin Cancer Detection
Human Facial Recognition
You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures.
Data Science is the hottest job of the 21st century. You need good programming skills analytical skills and years of hard work to be a Pro in Data science. This one-hour course is precise, to the point and efficient. It has no unnecessary details. This is the only course you need. We understand our students are Professionals and have limited time and limited attention span. Taking a few month's courses and forgetting everything along the way is not an efficient way to learn. We learn by practice.
Learn the most important aspect of Data Science :
Importing and working with Datasets
Building a Deep Convolutional Network Model using Keras
Compile, train, test and analyze the model
We will build a Traffic Sign Classifier using Keras. In this hands-on project, we will complete the following tasks:
Task 1: Project Overview
Task 2: Introduction to Google Colab and Importing Libraries
Task 3: Importing and Exploring Dataset
Task 4: Image Pre-Processing
Converting image to grayscale
Applying the histogram equalization technique
Task 5: Build a deep convolutional network model using Keras
Task 6: Compile and train the model
Task 7: Testing model with the test dataset & assess the performance of the trained Convolutional Neural Network model
Task 8: Saving the trained model
We’ll be carrying out our entire project in the Google Colab environment. That's why pre-installation of libraries and dependencies are not required.
Introduction to the Google Colab and Importing Necessary Libraries
Cloning, Exploring and Visualize Datasets
Image pre-processing that includes Grayscale conversion, Applying Histogram Equalization Technique and Image Normalization
Building Convolutional Neural Networks with Keras
Compile and Train a Deep Learning Model that can identify between 43 different Traffic Signs
Test the model with the test dataset & oversee the performance of the trained convolution neural network model
Basic Python Programming
Basics of Neural Network
Check out the detailed breakdown of what’s inside the course
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