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ML to LLMs

Interview prep

Seven-day interview plans for four roles. Each day picks the lessons, labs, cheat sheets, and interview questions that role is actually tested on.

ML Engineer

0/31 done

Seven days on what ML engineer loops test - modeling fundamentals, tree ensembles, deep learning, ML system design, and production.

7 days · 31 lessons

Open sprint Rapid-fire

AI Engineer

0/27 done

Seven days on what AI and LLM engineer loops test - how LLMs work, adaptation, RAG, agents, evaluation, inference, and security.

7 days · 27 lessons

Open sprint Rapid-fire

Applied Scientist

0/39 done

Seven days on what applied science loops test - derivations, modeling depth, deep learning, transformers, post-training, and experiment design.

7 days · 39 lessons

Open sprint Rapid-fire

Data Scientist

0/29 done

Seven days on what data science loops test - statistics, A/B testing, metrics, classical ML, and product cases, plus enough LLM literacy for today's teams.

7 days · 29 lessons

Open sprint Rapid-fire

Rapid-fire drill

Interview flashcards and timed quiz questions from any sprint, module, or the whole course.

Start drillingAll quizzes and review queue

Seven days, 60 to 120 minutes a day, aimed at one job family. Each sprint assumes you have some ML background. If a lesson is new to you, read it in full; if you're reviewing, switch the website to Quick read (intuition, lab, summary, and interview questions only).

How every sprint day works

  1. Read the day's lessons. Use quick read for review, full lessons for anything new.
  2. Play with the day's labs. Make a prediction before you touch a control, then check it.
  3. Answer out loud. Every lesson ends with an Interview angle section: questions real interviewers ask, with model answers. Say your answer before you open it.
  4. Drill. On the website, the rapid-fire drill mixes the interview flashcards and timed quiz questions for the sprint you pick. Cards you mark shaky come back later.

Day 7 is always a mock interview: a timed drill across the whole sprint, two design prompts, and a project story.

What these sprints don't cover

Coding rounds (LeetCode-style data structures and algorithms), SQL, and behavioral interviews. Practise those separately. The "implement it" challenges at the end of each lesson do cover ML coding questions such as writing k-means, logistic regression, or attention in NumPy.