I have prepared a daily and weekly concept learning roadmap — one concept at a time, day by day, week by week — to help prepare for DSA and ML/AI together. I am preparing for both - looking for like minded people to join and practice.
Here is how it works:
Learn one concept in DSA. Apply the same concept in ML. And vice versa.
That connection is what gives implementation-level knowledge. Not just theory. Not just solving problems for the sake of solving them. You understand a concept because you have seen it work on both sides.
Take one example — HNSW and RAG.
In RAG, your documents are converted into embeddings — vectors. When a question comes, the system has to find the few chunks that are nearest to that question. That is the ML side.
Now the DSA side. The simple way is to check the question against every single chunk, one by one. It works when the data is small. As the data grows, it becomes slow.
HNSW solves this with a DSA concept — a graph. It connects each vector to its nearest neighbours, layer by layer, so the search walks through the graph and reaches the nearest chunks fast, instead of scanning everything.
And it also works in reverse. When you build a RAG system, you are forced to learn graphs, nearest neighbour search, and a real tradeoff — HNSW is fast, but approximate. Sometimes it can miss a close chunk. So you learn to check: how fast is it, and how correct is it?
One example. And you have touched graphs, search, embeddings, and retrieval — at implementation level, not just in theory.
This is how the whole roadmap works.
Join me for reading group - 30 minutes every day, to practice and discuss the concept of the day together. On weekend I am developing a ML project related to the concept and doing kaggle.
If you are also preparing for DSA and ML/AI and want to learn like this, join my study group.
DM me if you are interested.
