Machine Learning & Self-Driving Cars: Bootcamp with Python

Looking to break into the exciting field of Machine Learning or Self-Driving Cars? This course provides a comprehensive understanding of how autonomous vehicles, like those developed by Tesla, function, presented in an accessible and engaging manner. Designed by a seasoned Data Scientist and Autonomous Vehicle expert, this course demystifies complex concepts and equips you with practical skills.

The course structure caters to all levels, offering a tiered learning approach:

• Introduction (Beginner): Grasp the fundamental concepts and initial intuition behind each topic.
• Hands-On (Intermediate): Apply your knowledge through practical lectures and real-world exercises.
• Deep Dive (Expert/Optional): Delve into the mathematical underpinnings for a complete understanding.

Key Tools & Technologies:

• Python: Master the versatile programming language essential for Machine Learning, Computer Vision, and more.
• Python Libraries: Unlock the power of libraries like matplotlib, OpenCV, numpy, scikit-learn, and Keras to expand your capabilities.
• Webots: Utilize this powerful, free, and open-source simulator to experiment with various scenarios, including Self-Driving Cars, drones, and robotic systems.

Who is this course for?

This course is suitable for learners of all levels, with no prior knowledge required. A high school level understanding of maths and logic is sufficient.

Course Sections:

• Python Programming: Learn the basics of Python and how to leverage essential libraries for data analysis and Machine Learning.
• Computer Vision (CV): Discover how computers "see" and explore key concepts for building Neural Networks, crucial for autonomous driving.
• Machine Learning (ML): Gain an introduction to Machine Learning, core concepts, and techniques for road sign classification, a vital component of self-driving car systems.
• Collision Avoidance: Understand how radar and lidar sensors are used in self-driving cars for collision avoidance and path planning. We'll explore the differences between approaches used by companies like Tesla and other manufacturers.
• Deep Learning: Apply your knowledge of CV, ML, and Collision Avoidance to build advanced Neural Networks, including an introduction to Behavioral Cloning.
• Control Theory: Explore the fundamental principles of control systems that underpin all engineering fields, including autonomous vehicle control. (Note: While beneficial, this section can be skipped if your primary interest is Machine Learning.)

About the Instructor:

This course is led by an experienced Data Scientist with a Master's degree in Robotic & Computer Vision. With over 8 years of experience working on self-driving projects for major companies in the autonomous vehicle space (motorbikes, boats, and cars), the instructor is passionate about efficient learning and has incorporated proven techniques into this course. The instructor's background provides unique insights into the practical challenges and advancements in the field of self-driving cars.