Build a Self-Driving Car with Python and Deep Learning

Curious about how self-driving cars see and navigate the world? This course demystifies the core deep learning concepts that power autonomous vehicles, guiding you through the practical steps of building your own. You will go from foundational principles to hands-on application, learning how to process road imagery, train models to make driving decisions, and integrate these components into a cohesive system. By the end, you'll have the practical skills to build and understand the software behind a simulated self-driving car. What you'll learn: - Understand the core principles of deep learning, neural networks, and computer vision. - Apply computer vision techniques with OpenCV to detect lane lines from road data. - Build and train Convolutional Neural Networks (CNNs) with TensorFlow and Keras to recognize traffic signs. - Develop a behavioral cloning model that learns to steer a vehicle by analyzing driving examples. - Practice data preprocessing and augmentation to improve your model's performance and robustness. - Learn about the ethical considerations and current challenges in autonomous driving technology. The course starts with the essential theory of machine learning before guiding you through written exercises to build each component of the driving system. You'll work with code snippets to bring your project to life. This course is designed for absolute beginners. No prior experience in machine learning, deep learning, or Python is required to get started. Begin your journey into the world of autonomous systems today.