There is more free computer science on YouTube than anyone could watch. The hard part is picking a few good playlists and finishing them. This post lists 15 playlists from university courses and well-known teachers, grouped by topic, plus a study order so you do not jump around.
What you need
A free YouTube account, so you can save playlists and track progress.
A notebook or notes app, and a free GitHub account to push your practice code.
For DSA practice: a free account on a coding practice site such as LeetCode.
Step by step
1. Data structures and algorithms (DSA)
Start here if you are preparing for placements or coding interviews.
Algorithms by Abdul Bari: clear, whiteboard-style explanations of classic algorithms. Good first pass for college-level concepts.
MIT 6.006 Introduction to Algorithms, Spring 2020 (MIT OpenCourseWare): the full university course, with more depth and proofs.
Graph Series by Striver (take U forward): interview-focused graph problems in C++ and Java.
Dynamic Programming Playlist by Striver (take U forward): recursion to tabulation, problem by problem.
2. System design
Do this after you are comfortable with DSA and have built at least one small app.
System Design Fundamentals by ByteByteGo: short animated explainers on caching, load balancing, databases and more.
System Design Playlist by Gaurav Sen: concepts and classic design questions, explained in an interview style.
MIT 6.824 Distributed Systems, Spring 2020: the university course behind a lot of system design ideas (replication, consensus, fault tolerance). Harder, but worth it for backend roles.
3. Backend engineering and databases
Backend Engineering (Beginner) by Hussein Nasser: how backends actually talk to clients, protocols and common patterns.
Backend Engineering (Advanced) by Hussein Nasser: deeper topics once the basics are clear.
The Backend Engineering Show by Hussein Nasser: discussion episodes on real-world backend topics.
CMU Intro to Database Systems, Fall 2023 (CMU Database Group): how databases work inside, from storage to indexes and transactions.
4. Machine learning and AI
Neural networks by 3Blue1Brown: visual intuition for how neural networks learn. A good first step.
Stanford CS229: Machine Learning, Autumn 2018 (Andrew Ng): the classic maths-heavy ML course.
Neural Networks: Zero to Hero by Andrej Karpathy: build neural networks and a small GPT-style model from scratch in Python.
Stanford CS224N: NLP with Deep Learning, 2023: language models and transformers, at university level.
5. A simple order to follow
Pick one DSA playlist and solve problems alongside it. Do not just watch.
Build one small backend project (an API with a database) while watching the beginner backend playlist.
Move to system design once you have built something real. The ideas make more sense when you have hit the problems yourself.
Add the ML track only if you are aiming for ML or AI roles. Start with 3Blue1Brown, then Karpathy or CS229.
Keep a short notes file per playlist: one line per video on what you learned. It doubles as revision material before interviews.
Good to know
All of these are free to watch. Some university courses have extra material (assignments, notes) on their own course websites.
University lectures are long and assume some maths. If one feels too hard, finish an easier playlist on the same topic first.
Some courses are a few years old. The core ideas still hold, but specific tools and versions may have changed.
Watching is not learning. Plan to spend at least as much time coding and solving problems as you spend on videos.
These are independent picks, not sponsored. Playlist titles and video counts can change if the channel edits them.
Sources
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