Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.87 GB | Duration: 6h 30m
Combine the power of Machine Learning, Deep Learning and Computer Vision to make a Self-Driving Car!
What you’ll learn
Learn how to apply Machine Learning algorithms to develop a Self-Driving Car from scratch
Simulate a Self-Driving car in a realistic environment using multiple techniques (Computer Vision, Convolution Neural Networks, …)
Understand how Self Driving Cars work (sensors, actuators, speed control, …)
Learn about Computer Vision in a practical way, starting from simple examples until you are able to create an algorithm to drive a Self-Driving Car
Gentle introduction to Machine Learning, all the key concepts are presented in an intuitive way
Explain why Deep Learning is such a powerful ch and use it to make the car drive like a human (Behavioural Cloning)
Code Deep Convolutional Neural Networks with Keras (the most popular library)
Build, train and evaluate multiple models, from classic Machine Learning to Deep Neural Networks
How to code in Python starting from the very beginning
Python libraires: NumPy, Sklearn (Scikit-Learn), Keras, OpenCV, Matplotlib
Requirements
No programming experience needed. You will learn everything you’ll need to know.
Description
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, so that I could share 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
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
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
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 7 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
Who this course is for
All-levels, every section is separated with three levels: Introduction, Hands-On, Deep Dive
There is a Python section, so you do NOT need prior programming experience
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