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This is great. Thank you for the detailed answer.
I found two 6-course specializations on Coursera earlier, which one do you think would be a good starting point for me?
⬐ serenI don't known them, but it looks like they both introduced the same concepts, so either of them is probably right.
There is another Coursera class for aerial applications. Course 1 of the Penn State specialization:
A while back I wanted to learn about SLAM, computer vision, robotics, drones, etc. I did all the following courses which had significant overlap in materials:
And I would rank them in the order of edx, coursera, and udacity last.
It's not totally fair because the best comparison for udacity should be their self driving car nanodegree but it doesn't let you audit for free and I don't care for certificates. From the few udacity courses I did do, I felt their videos are too short and triggers my ADHD to go do something else after watching each minute or two long video. Edx/coursera felt a lot more like university lectures and felt more rigorous in comparison.
⬐ capitalsigmaI found edX to be pretty lacking if you wanted to learn something advanced that's not quite "flavor of the day:" no higher level pure math classes, very few algorithms classes past intro level, very little material on digital design other than the MIT 6.002 sequence. Tons of choices for machine learning, Python, and AP calculus though.⬐ barry-cotter⬐ ericlavigneThere’s a reasonably extensive MicroMasters course on Algorithms and Data Structures. How would you rate it? Comparable to finishing Skiena? CLRS? Concrete Mathematics?
What You'll Learn:
Understand essential algorithmic techniques and apply them to solve algorithmic problems Implement programs that work in less than one second even on massive datasets Test and debug your code even without knowing the input on which it fails Formulate real life computational problems as rigorous algorithmic problems Prove correctness of an algorithm and analyze its running time
Courses Algorithmic Design and Techniques Learn how to design algorithms, solve computational problems and implement solutions efficiently.
Data Structures Fundamentals Learn about data structures that are used in computational thinking – both basic and advanced.
Graph Algorithms Learn how to use algorithms to explore graphs, compute shortest distance, min spanning tree, and connected components.
NP-Complete Problems Learn about NP-complete problems, known as hard problems that can’t be solved efficiently, and practice solving them using algorithmic techniques.
String Processing and Pattern Matching Algorithms Learn about pattern matching and string processing algorithms and how they apply to interesting applications.
Dynamic Programming: Applications In Machine Learning and Genomics Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.
Graph Algorithms in Genome Sequencing Learn how graphs are used to assemble millions of pieces of DNA into a contiguous genome and use these genomes to construct a Tree of Life.
Algorithms and Data Structures Capstone Synthesize your knowledge of algorithms and biology to build your own software for solving a biological challenge.Agreed that Udacity's AI for Robotics was a weak course. I highly recommend some of Udacity's recent paid-only courses on robotics:
Self-driving car engineer nanodegree (not the one with "intro" in its name) https://www.udacity.com/course/self-driving-car-engineer-nan...
Robotics software engineer nanodegree https://www.udacity.com/course/robotics-software-engineer--n...
Flying car and autonomous flight engineer nanodegree https://www.udacity.com/course/flying-car-nanodegree--nd787
Just as a sample, this is one of many projects I completed as part of the self-driving car engineer nanodegree. My code controls a car driving on a highway with other cars.
Robotics, UPenn: https://www.coursera.org/specializations/robotics
Excellent introduction to the algorithms that underlie control systems for robots. For the assignments, you program Matlab simulators of robots. It is comprehensive and not dumbed down: plenty of calculus involved! I loved it.