Virtual Driverless Car

Virtual Driverless Car

6,999 / 20 Sessions

Say hello to autonomous driven vehicles | Ages 14 & above

Categories: ,
  • Duration: 20 Sessions

Virtual Driverless Car is an innovative course that deals with leading-edge of robotics, artificial intelligence, machine learning and neural networks. It is an interactive course where students get an overview of all these new technologies. Students get to understand the evolution of self driving cars, Image processing, Basics of Neural networks during this course.

  • Age range: 14 and above
  • Level: Beginner
  • Duration: 20 Sessions x 2Hrs
  • Mode: Online and Self-Learning
  • Learning Content: Rich & Experiential
  • # of lessons: ~ 5 lessons
  • # of quizzes: Nil
  • Learning Experience: Hands-On
  • Concepts: AI, Machine Learning, Neural Networks
  • Coding Tools: Python
  • Capstone Projects: Build a Self driving car in Simulator
  • Subscription validity: One year
  • Course Certificate on Completion
  • Membership to AIWS Community

List of courses

Themes and Modules Learning Outcomes
What is AI? This experiential course provides students with an overview and concepts of Artificial Intelligence
Do we need self-driving cars? Describes a self -driving car and its applications.
Are there different levels of self driving? Explains about the US National Highway Traffic Safety Administration
Are Sensors available in self-driving cars? Discover various sensors in Autonomous Vehicle
Does computer vision have? Analyse an computer vision tools to detect the lane lines on roads
What is Machine Learning? Model and nurture creativity and creative expression by understanding Machine Learning
Is Advance Lane Tracking in video? Identifies the pipeline and track the position of lane lines in video stream using detection techniques
What is Neural Networks? Learn about more input, output and hidden layers
What is the MNIST Dataset? Explore more about the handwritten digit classification
Do we need a traffic-sign recognizer? Build and experience the summarization, distribution and validating the dataset