September last year, I was interviewing at a Pune-based AI startup. They asked me how SVM works. I said: "it finds the maximum margin hyperplane separating the classes." They nodded and asked me to formulate the optimization problem. I went blank.
Same thing happened with decision trees — I knew what they did, I just couldn't explain why the algorithm makes the choices it does mathematically.
I didn't get the role.
Indian startup interviews, especially at product and AI-focused companies, go deeper than you expect. They don't want you to just name the algorithm — they want the math. The Lagrangian, the dual formulation, why support vectors are the only points that matter. Sklearn knowledge alone won't save you.
So I went back and wrote it out properly, from scratch. Here's the SVM chapter — hard margin, soft margin, kernel trick, Mercer's condition, the full dual derivation. No hand-waving.
Link below: https://drive.google.com/file/d/1P0o1KQrMrjxcqJO_hYKMkJoUIH9EuH5D/view?usp=sharing
If you're preparing for ML roles at Indian startups and rely on intuition-only resources, this is the gap they will find.