Data Science: Hands-On 1 Hour Project On Deep Learning

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 Data ScienceHands-On 1 Hour Project On Deep Learning. Image Classification for Autonomous Vehicle using Keras : Learn to explore datasets and build, train and test models.

What you’ll learn

  • 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 model with the test dataset & oversee the performance of trained convolution neural network model


  • Basic Python Programming
  • Basics of Neural Network


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.

Although, for the sake of this project we will using 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 that hundreds of hours of unnecessary theoretical lectures.

Data Science is the hottest job of the 21st century.  You need good programming skills and 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

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