Learn the basics of Self-Driving Cars in a simplified manner | Autonomous Vehicles
Sub Category
- Data Science
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Objectives
- Need for Self-Driving Car
- History of Self-Driving Car
- Human Brain & Artificial Neural Network
- Foundations of AI & Machine Learning [ML]
- Type of ML - Supervised , Unsupervised, Reinforcement Learning
- Role of ML in Self-Driving Car / Autonomous Vehciles
- Perception Module
- Localization Module
- Path Planning Module
- Motion Control Module
- Case Study I - Waymo System Overview
- Case Study I - Waymo Perception, Localization, Path Planning, & Motion Control Module
Pre Requisites
- Basic Understanding of Artificial Intelligence: A fundamental grasp of AI principles will be beneficial. This includes familiarity with basic AI concepts and terminology. Don't worry if you're not an expert – a general awareness is sufficient to get started.
- Math Fundamentals: A comfort with numbers, especially basic algebra and statistics, will help you understand the tech behind the wheel.
- Google Colab Access: Not a must-have right now, but having this tool in your kit will be handy for later sections
- Enthusiasm for Autonomous Vehicles: Your interest and passion for self-driving cars are your best assets.
- Open Mind and Curiosity: Stay curious and open to new ideas – they're the fuel for innovation in this field.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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