The edX version of MIT's computational thinking courses, split into two parts.
6.00.1x covers Python fundamentals and algorithmic thinking; 6.00.2x goes deeper
into data structures, stochastic thinking, and simulation. A solid alternative to
the OCW version if you prefer a more structured format with graded assignments.
Free to audit (full access to all materials). Verified certificate available for
an additional fee if needed.
[Enroll on edX](https://www.edx.org/xseries/mitx-computational-thinking-using-python)
A comprehensive playlist covering graph theory and algorithms from scratch — DFS,
BFS, Dijkstra, Bellman-Ford, Floyd-Warshall, minimum spanning trees, max flow,
and more.
Directly relevant to the **Network Algorithms course (L1-S2)** at UFAZ, which
covers graph representations, routing problems, Prim, Ford-Fulkerson, Edmonds-Karp,
Dinic, Dijkstra, Bellman-Ford, and matching algorithms. Watch this alongside or
before that course.
[Watch on YouTube](https://www.youtube.com/playlist?list=PLDV1Zeh2NRsDGO4--qE8yH72HFL1Km93P)
A beginner-friendly overview of generative AI — how LLMs work, prompt engineering
basics, and real-world applications. Pair with AI for Everyone as an entry point
before moving into the ML or Deep Learning specializations.
[Enroll on Coursera](https://www.coursera.org/learn/generative-ai-for-everyone)
A non-technical introduction to AI — what it can and can't do, how ML projects
work, and how to think about AI strategy. Start here if you're completely new
to the field before jumping into any technical courses.
[Enroll on Coursera](https://www.coursera.org/learn/ai-for-everyone)
Good platform for practicing English reading and listening. Worth using regularly to build consistency,
but keep in mind these are general English materials — actual IELTS exam texts and listening tracks
tend to be less complex than what you'll find here.
For official IELTS preparation, pair this with the Cambridge IELTS books and the British Council / IDP
practice materials.
The official textbook companion to MIT's 6.100L course. Covers computational thinking, Python programming,
algorithms, data structures, and basic statistics — written by John Guttag, one of MIT's core CS educators.
Worked through this alongside my internship at NAIC. More structured than the lecture slides alone —
worth having open while going through the OCW course.
MIT's official introductory CS and Python course, freely available on OpenCourseWare. Covers Python fundamentals,
computational thinking, algorithms, recursion, data structures, and object-oriented programming — with full
lecture slides, problem sets, and exams included.
Went through this during my internship at NAIC. Solid foundation before jumping into anything ML-related.
Best math and ML intuition channel on YouTube. Covers linear algebra, calculus, neural networks, and transformers with unmatched visual clarity. Essential before or alongside any serious ML coursework.