What you'll learn:
- Master Machine Learning and Python
- Learn how to apply Machine Learning algorithms to develop a Self-Driving Car from scratch
- Understand why Deep Learning is such a revolution and use it to make the car drive like a human (Behavioural Cloning)
- Simulate a Self-Driving car in a realistic environment using multiple techniques (Computer Vision, Convolution Neural Networks, ...)
- Create strong added value to your business
- Gentle introduction to Machine Learning where all the key concepts are presented in an intuitive way
- Code Deep Convolutional Neural Networks with Keras (the most popular library)
- Learn to apply Computer Vision and Deep Learning techniques to build automotive related algorithms
- Understand how Self Driving Cars work (sensors, actuators, speed control, ...)
- Learn to code in Python starting from the very beginning
- Python libraires: NumPy, Sklearn (Scikit-Learn), Keras, OpenCV, Matplotlib
Interested in Machine Learning or Self-Driving Cars (i.e. Tesla)? Then this course is for you!
This course has been designed by a professional Data Scientist, expert in Autonomous Vehicles, with the goal of sharing my knowledge and help you understand how Self-Driving Cars work in a simple way.
Each topic is presented at three levels:
Introduction:the topic will be presented, initial intuition about it
Hands-On:practical lectures where we will learn by doing
[Optional] Deep dive: going deep into the maths to fully understand the topic
What tools will we use in the course?
Python: probably the most versatile programming language in the world, from websites to Deep Neural Networks, all can be done in Python
Python libraries:matplotlib, OpenCV, numpy, scikit-learn, keras, ... (those libraries make the possibilities of Python limitless)
Webots: a very powerful simulator, which free and open source but can provide a wide range of simulation scenarios (Self-Driving Cars, drones, quadrupeds, robotic arms, production lines, ...)
Who this course is for?
All-levels:there is no previous knowledge required, there is a section that will teach you how to program in Python
Maths/logic:High-school level is enough to understand everything!
Sections:
[Optional] Python sections: How to program in python, and how to use essential libraries
Computer Vision: teaches a computer how to see, and introduces key concepts for Neural Networks
Machine Learning:introduction, key concepts, and road sign classification
Collision Avoidance: so far we have used cameras, in this section we understand how radar and lidar sensors are used for self-driving cars, use them for collision avoidance, path planning
Help us understand the difference between Tesla and other car manufacturers, because Tesla doesn’t use radar sensors
Deep learning:we will use all the concepts that we have seen before in CV, in ML and CA, neural networks introduction, Behavioural Cloning
Control Theory:control systems is the glue that stitches all engineering fields together
If you are mainly interested in ML, you can only listen to the introduction for this section, but you should know that the initial Neural Networks were heavily influenced by CT
Who am I, and why am I qualified to talk about Self-driving cars?
Worked in self-driving motorbikes, boats and cars
Some of the biggest companies in the world
Over 8 years experience in the industry and a master in Robotic & CV
Always been interested in efficient learning, and used all the techniques that I’ve learned in this course
