❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [October 07th, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)
Welcome to Self-Driving Simulations: Developing Autonomous Cars with Python course This is basically an extensive project based course where you will be fully guided step by step on how to build autonomous vehicle simulation with self driving feature using Python programming language alongside with Python libraries such as Pygame and NEAT where Pygame will be utilised to create a visual and realistic representation of the simulated environment while NEAT which stands for NeuroEvolution of Augmenting Topologies will be used to train the neural networks to control and design self driving behaviour The neural network takes input from the cars sensors In addition the neural network will also learn and adapt over time through evolutionary algorithms improving the cars driving performance and decision-making skills In the introduction session you will be learning the basic fundamentals of autonomous car getting to know technologies behind it as well as understanding how it works Then after learning the basic concepts you will be guided step by step to set up all necessary tools for instance Visual Studio Code IDE installing Python and other relevant tools Before getting into the project there will be a basic python training session where you will learn all important concepts in Python that you need to know and master to prepare you for the upcoming project The basic Python training session is optional since the session was created and intended only for those who are not very confident with their Python programming skills In the basic Python training session you will learn different data types or variables how to build functions and pass down parameters to the function how to build class and basic fundamentals of Pygame Then once the basic Python training session has been completed you will move to the project where you will be fully guided step by step on how to build an autonomous car simulation with advanced self driving features from scratch Once the project has been built we are going to be conducting testing not only to test if the code works but also to test if the output of the code is what we expected to get There will be three main objectives that will be tested those are the cars decision making ability sensor integration and collision preventionFirst of all we need to ask ourselves this question Why should we learn how to build an autonomous car simulator? It might be very interesting to learn how the self-driving feature in cars like Tesla works obviously the system is very complicated and a bit difficult to be understood but what if you have a chance to learn the self driving mechanism from a more simple perspective and that's exactly what you are going to learn in this course The next follow up question might potentially be well it is near impossible and definitely unrealistic to create my own real autonomous vehicle like Tesla it will cost you a lot and even if you have the funding you might not have the right skill sets and knowledge to begin with That is actually true to some extent we are not going to build a brand new car with self -driving features instead we can potentially build a very cool self-driving game or maybe build an autonomous object simulatorBelow are things that we are going to learn from this course:Learning the fundamental concepts of self driving autonomous car getting to know technologies behind it as well as its capabilities and limitationsLearning and understanding how autonomous car worksBasic Python training session which prepares you better for the autonomous car projectBuilding self driving autonomous car simulation project using Pygame and NEATLearning how to build and design car track using GIMP painting toolTesting the self driving autonomous cars to ensures the car has a good decision making ability solid sensor integrations and effective collision prevention system

We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
(Please note that our content is optimized through artificial intelligence tools and carefully reviewed by our editorial staff.)
What skills and knowledge will you acquire during this course?
During this course, you will acquire the following skills and knowledge:
1. Understanding the fundamental concepts of self-driving autonomous cars, including the technologies behind them and their capabilities and limitations.
2. Learning how autonomous cars work, including the role of neural networks and sensors in controlling and designing self-driving behavior.
3. Basic Python training session to ensure you have a strong foundation in Python programming.
4. Building a self-driving autonomous car simulation project using Python libraries such as Pygame and NEAT.
5. Using Pygame to create a visual and realistic representation of the simulated environment.
6. Utilizing NEAT (NeuroEvolution of Augmenting Topologies) to train the neural networks for the self-driving behavior.
7. Learning how to design and build car tracks using GIMP painting tool.
8. Testing the self-driving autonomous cars to evaluate their decision-making ability, sensor integration, and collision prevention system.
Who will benefit from this course?
This course will benefit individuals who are interested in learning about self-driving technology and developing autonomous car simulations. It is suitable for individuals with a background in Python programming or those who want to improve their Python skills.
Specific professions that may benefit from this course include:
1. Software Developers: This course provides hands-on experience in building autonomous car simulations using Python and relevant libraries. Software developers can enhance their programming skills and gain practical knowledge in developing self-driving features.
2. Data Scientists: The course covers neural networks and evolutionary algorithms used in training the autonomous car's decision-making abilities. Data scientists can learn how to apply these techniques to improve the car's performance and decision-making skills.
3. Game Developers: The course utilizes Pygame to create a visual and realistic representation of the simulated environment. Game developers can learn how to build self-driving games or incorporate autonomous features into their existing games.
4. Automotive Engineers: Although this course does not focus on building real autonomous vehicles, it provides a simplified perspective on the self-driving mechanism. Automotive engineers can gain insights into the fundamental concepts and technologies behind autonomous cars.
5. Robotics Enthusiasts: Individuals interested in robotics and autonomous systems can benefit from this course by learning how to develop self-driving behaviors and simulate autonomous objects.

Course Syllabus

Introduction

Tools, IDE, and Libraries

Introduction to Autonomous Cars

Setting Up All Required Tools

Basic Python Training Session

Preparing Assets for Autonomous Car Simulation

Building Self Driving Autonomous Car Project

Testing Autonomous Car Simulation

Conclusion & Summary