#969 overall
How to Build Your Own Self-Driving Vehicle
Udacity
1 recommender · 1 thread · recommended in 1 year, 2019 · score 0.7
A Udacity course, originally offered in 2012, that walks learners through building a self-driving vehicle in Python, covering topics such as SLAM, path finding, and Kalman filtering. It grew out of Udacity's early AI courses.
One commenter, who took it after the 2011 ML Class, says it taught them concepts they had struggled with before, including SLAM, A*-style path finding, and Kalman filtering, and calls the earlier series an excellent course.
Machine Learning & AIintermediatefreevideo lectures#self-driving cars#robotics#slam#kalman filter#path finding#python
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I second this suggestion; I took that course when it was called "ML Class" during the Fall of 2011 (yep, I was one of the guinea pigs for what became one of the first courses of Coursera). It was an excellent course.
Here's an example of what one student of the ML Class built, after being inspired by what he was learning and videos that played during the course:
https://blog.davidsingleton.org/nnrccar/
It kinda shocked me at the time, because I knew quite a bit about ALVINN from books and articles I had read as a teenager in the 80s and 90s. This guy had created the same thing using a cell phone and a cheap RC vehicle! Ok, there was also an Arduino and computer involved - but it really hit home the fact that technology around neural networks had advanced quite a bit!
I also took the other course, "AI Class", but due to personal issues I had to drop out about halfway through.
The next year, after Udacity started, they introduced a course similar to AI Class called "How to Build Your Own Self-Driving Vehicle" (it's called something else today - something like "Robotics and Artificial Intelligence 302" or something like that).
That class was done in Python, and taught me even more about AI/ML - with a focus towards self-driving vehicles of course. Things I learned about that I struggled with or had no real concepts of before:
1. SLAM (Simultaneous Localization and Mapping) 2. Path Finding algorithms (A* and the like) 3. Kalman Filtering (what it is for, how it works) 4. PID Algorithm (how to implement and tune it) 5. More neural network stuff...
...and many other things. Another very excellent and free course to take if you're interested in learning this stuff.