Course Outline


Setting up the Development Environment

Creating a Project

Configuring the Simulator

Preparing the Data Sets

Overview of Python Deep Learning Libraries

Applying Computer Vision Techniques to Track Lanes

Training Perceptron-Based Neural Networks to Detect Other Vehicles

Implementing Convolutional Neural Networks to Predict Steering Angle and Speed

Training a Deep Learning Model to Classify Traffic Signs

Using Polynomial Regression to Improve Predictive Accuracy

Testing the Self Driving Car


Summary and Conclusion


  • Python programming experience.


  • Developers
  21 Hours


Related Courses

Deep Learning for Vision with Caffe

  21 hours

Computer Vision with Python

  14 hours

Computer Vision with SimpleCV

  14 hours

Real-Time Object Detection with YOLO

  7 hours

YOLOv7: Real-time Object Detection with Computer Vision

  21 hours

Deep Learning with Keras

  21 hours

Advanced Deep Learning with Keras and Python

  14 hours

Marvin Framework for Image and Video Processing

  14 hours

Computer Vision with OpenCV

  28 hours

Python and Deep Learning with OpenCV 4

  14 hours

Pattern Matching

  14 hours

Raspberry Pi + OpenCV for Facial Recognition

  21 hours

Hardware-Accelerated Video Analytics

  14 hours