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Description of  Machine Learning & Self-Driving Cars Bootcamp with Python

Genre: eLearning | MP4 | Video: h264, 1280x720 | 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 multmachine-learning-self-driving-carsiple 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, MatDescriptionlib Requirements No programming experience needed. You will learn everything you'll need to know. Description 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 presentecoursed 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 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 Comwww.udemy.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 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 Homepage https: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: matDescriptionlib, 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