> The best technical courses are on Udacity.
Is that universally agreed on? The Udacity courses I had looked at (full stack development, AI for self driving cars) were good and insightful, but somewhat half-baked, and not as good as edX or Coursera.
Is that universally agreed on? The Udacity courses I had looked at (full stack development, AI for self driving cars) were good and insightful, but somewhat half-baked, and not as good as edX or Coursera.
[1] Earlier comment where I asked a basic question that went beyond the understanding of the literal face of the course. https://news.ycombinator.com/item?id=18596450#18601298
I would be interested (if you have the energy) to see which Coursera and Edx courses you preferred to the Udacity courses you liked.
https://www.edx.org/course/autonomous-navigation-flying-robo...
https://www.coursera.org/specializations/robotics (also available at edx as https://www.edx.org/micromasters/pennx-robotics)
https://in.udacity.com/course/artificial-intelligence-for-ro...
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.
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.
https://www.edx.org/micromasters/ucsandiegox-algorithms-and-...
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.