Data Science: Deep Learning Project for Self Driving Cars

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

• Currency 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

  •      Normalization

• 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.