Intro to Self-Driving Cars is an introductory self-driving car training course published by Udacity Specialized Academy. This 4-month training course is one of the most important and complete courses available in the field of self-driving cars on the Internet, taught by a group of experienced experts.
This training course contains a series of scattered but very practical topics such as Python and C++ programming languages, matrices and applied calculations, computer vision, and machine learning. Is. All these topics are used in solving problems and challenges in the field of self-driving cars, and by putting together the potentials of each, we will be able to develop 100% self-driving and intelligent cars.
This training course is not recommended for beginner students and the student is expected to have some background knowledge in the fields of introductory algebra and reading and editing Python and C++ codes.
What you will learn in the Intro to Self-Driving Cars training course:
- Bayesian inference
- Principles and basics of self-driving car design
- Ways of understanding and observing the environment by self-driving cars
- Object-oriented programming and its importance in the development of self-driving cars
- Linear algebra and matrices
- The basics of Python and C++ programming languages
- Complex data structures
- Algorithm writing and algorithmic thinking
- Advanced and functional accounts
- Using different libraries and frameworks based on Python for visualization of data and mathematical problems
- machine learning
- computer vision
- And …
Intro to Self-Driving Cars Course details
Instructors: Sebastian Thrun , Andy Brown , Cezanne Camacho , Andrew Paster , Anthony Navarro , Elecia White and Tarin Ziyaee
Education level: Intermediate
Number of lessons: 41
Duration of training: assuming 10 hours of work per week, about 4 months
Part 01: Orientation
Module 01: Welcome to Intro to Self-Driving Cars!
Module 02: Readiness
Part 02: Bayesian Thinking
Module 01: Bayesian Thinking
Part 03: Working with Matrices
Module 01: Working with Matrices
Part 04: C++ Basics
Module 01: C++ Basics
Part 05: Performance Programming in C++
Module 01: Performance Programming in C++
Part 06: Navigating Data Structures
Module 01: Navigating Data Structures
Part 07: Vehicle Motion and Control
Module 01: Vehicle Motion and Control
Part 08: Computer Vision and Machine Learning
Module 01: Computer Vision and Machine Learning
Part 09: Graduation!
Module 01: Congratulations!
Module 02: Guaranteed Admission into your next Nanodegree
Prerequisites of the Intro to Self-Driving Cars course
Students should have some experience with programming—writing short scripts in a programming language—and be comfortable with algebra. You should also feel comfortable reading and modifying the code that you are given, with the understanding that solving problems in code may still be challenging. If you believe you need more preparation, here are some additional resources you can use:
- Introduction to Computer Science
- Programming Foundations with Python
- Intro to Statistics, Descriptive Statistics, and Inferential Statistics
- Linear Algebra Refresher
- Intro to Data Science and Data Analysis
- Statistics and Probability (Khan Academy)
- Intro to Programming Nanodegree Program
- Data Analyst Nanodegree Program
What software and versions will I need for this program?
For this Nanodegree program, you will need to have the minimum equipment requirements outlined here: https://www.udacity.com/tech-requirements.
Which version of TensorFlow, Keras, ROS, and C++ are taught in this program?
- TensorFlow Version 1.3
- Keras version 2
- ROS Kinetic
- Python Version 3
- C++ Version 11
Intro to Self-Driving Cars Course images
In order to view the courses of the course in an organized and regular way, run the index.html file and run the videos through this file.