Resources

The vetted toolkit. Every link here was checked by an adversarial link-checker — dead and hallucinated links were removed. ★ = start here. Free unless marked [paid].

DSA (Season 1 — pairs with Striver)

• ★ Striver's A2Z DSA Sheet — the spine: 474 problems / 18 steps, strict easy→hard, built-in progress tracking.
• Striver A2Z YouTube playlist — lectures mapping 1:1 to the sheet, full intuition + dry-run before code. Use when a step doesn't click.
• take U forward channel — topic deep-dive series (DP, Graphs, Trees, Binary Search) when you want to drill one area harder.
• NeetCode Roadmap — visual dependency graph of which topic unlocks which. Great prerequisite check.
• NeetCode 150 — tighter 150-problem pattern set with clean video solutions. Your second pass.
• Sean Prashad's LeetCode Patterns — problems grouped by pattern + constraint→technique heuristics. The free Grokking stand-in.
• Tech Interview Handbook — Coding Interview Techniques — readable prose on the classic coding patterns.
• Big-O Cheat Sheet — one-glance complexity table. Keep it open; state complexity for every answer.
• CP-Algorithms — rigorous reference for the deep theory (segment trees, number theory, graph algos) interview sites skip.
• Anki — spaced repetition. One card per problem capturing the pattern/trigger, not the code.
• Grind 75 — hours-aware spaced study planner if you want to pace review.
• NeetCode Advanced Algorithms [paid] — Pro course for harder algo theory (only ~10-min previews are free).

System design (Season 2)

• ★ The System Design Primer — free, 350k★ canonical starting map: study plan, flashcards, worked problems. Start here before paying for anything.
• ByteByteGo YouTube — short animated explainers of core building blocks. Lowest-friction intuition.
• Gaurav Sen (@gkcs) — ex-Uber engineer reasoning out loud through whiteboard problems.
• awesome-system-design-resources — actively maintained free index (concepts + must-read articles). The free Grokking alternative.
• awesome-scalability — the best single collection of real-world architecture writeups (Netflix, Uber, Google). Your case-study firehose.
• System Design Interview Vol. 1 & 2 — Alex Xu [paid] — the de facto interview bible: a repeatable framework + ~30 worked designs.
• Grokking the System Design Interview [paid] — guided drills (now Design Gurus, no longer Educative).
• Designing Data-Intensive Applications — Kleppmann [paid] — the deep authority on replication, partitioning, consistency, consensus. Read it last to turn patterns into real understanding.

Math foundations (for ML and quant)

• ★ 3Blue1Brown — Essence of Linear Algebra — geometric intuition for vectors, transformations, eigenstuff before any formalism. Watch first.
• Mathematics for Machine Learning — the single best free book tying linear algebra, calculus, probability, and optimization to ML.
• Stat 110: Probability (Blitzstein, Harvard) — the canonical free probability course (34 lectures), problem-solving style that maps to quant brainteasers.
• Intro to Probability — Blitzstein & Hwang (problem sets) — 250+ practice problems with solutions; the bridge to quant interview probability.
• MIT 18.065 — Matrix Methods (Gilbert Strang) — SVD, gradient descent, the optimization math under ML. The deeper second pass.

Quant & puzzles (the hard-problem arena)

• ★ Jane Street Puzzles (archive) — the literal Jane Street style: monthly puzzles + full archive with solutions.
• Project Euler — math-meets-code problems building computational number-theory/combinatorics muscle.
• Putnam Competition archive (MAA) — Putnam-style proof rigor, hardest tier; sharpens the same muscles quant firms probe.
• A Practical Guide to Quantitative Finance Interviews — "Green Book" [paid ~$27] — 200+ real quant problems (brainteasers, probability, stochastic calc). The one paid item worth buying.
• Brilliant [freemium] — interactive daily-problem habit for probability and logic. Warmup layer, not the core.

Core ML

• ★ Machine Learning Specialization — Andrew Ng — the canonical first ML course (auditable free). Supervised learning + intuition.
• Practical Deep Learning for Coders — fast.ai — top-down, code-first complement to Ng. Ship working deep models early. Fully free incl. book.
• The Hundred-Page ML Book — Burkov — concise map of the whole ML landscape. Connective-tissue text between courses.
• Elements of Statistical Learning — Hastie et al. — the rigorous statistical-learning bible (official free PDF). Depth pass once the math is in.

ML deep-dive fields (Season 5+ — pick one)

• Stanford CS224N — NLP with Deep Learning — transformers, attention, LLMs. Pair with Jurafsky & Martin (free).
• Stanford CS231n — Deep Learning for Computer Vision — CNNs, detection, ViTs. Free self-contained notes.
• OpenAI Spinning Up in Deep RL — best free hands-on RL onramp. Pair with David Silver's RL lectures.
• Google Recommendation Systems crash course — candidate generation, retrieval, ranking.
• Made With ML — MLOps — end-to-end productionizing ML (testing, CI/CD, serving, monitoring). The engineering-track field.

The deep roadmaps in roadmaps/ go far beyond this list — staged paths, hard problem sets, projects, and curiosity hooks per domain.