A complete, opinionated mentoring framework for instructors and self-learners — from first notebook to fine-tuned transformer. Twelve modules per course, weekly plans, rubrics, capstones, and a real career runway.
Our flagship module replaces matrix-dense lectures with intuition, diagrams, and incremental code. Students implement scaled dot-product attention by week 3 and ship a fine-tuned transformer by week 8.
Read the full course framework →Each course ships with the full 12-module framework — never just a slide deck.
Tooling, notebooks, pandas, EDA
Supervised, unsupervised, evaluation
Bag-of-words, embeddings, cosine sim
Naive Bayes, HMMs, n-grams
RNNs, LSTMs, GRUs
Search, logic, agents
Pipelines, MLOps basics
Backprop, CNNs, regularization
BERT, GPT, fine-tuning, RAG
Bayesian, GNNs, RL primer
End-to-end production project
Consistency is what turns isolated lectures into a mentoring program. Here's the spine — applied identically across all nine courses.
Pre-requisites, mastery checklist, industry outcomes.
Week-by-week and daily lesson plans.
Analogies, visuals, common-mistake catalog.
Exercises, mini-projects, Kaggle drills.
Office hours, project reviews, struggling-student playbooks.
Libraries, GPU/cloud, GitHub & notebook hygiene.
Quizzes, rubrics, interview-style questions.
Resume-grade portfolio builds.
Interviews, LinkedIn, AI career paths.
Books, papers, datasets, channels.
Skills dependency map and timelines.
Transformers, attention visuals, research mentoring.
Diagnose blockers in 3 questions. Pair-program. Re-explain with a fresh analogy, never the same one twice.
Ship something visible every week. Celebrate working code over perfect code.
Open with 'What's the smallest thing that isn't working?' — keeps sessions concrete.
Score on: reproducibility, clarity, evaluation rigor, and one surprising insight.
Ask students to teach back. If they can explain it to a peer, they own it.
All 9 courses — concepts, formulas, runnable code, and exercises — laid out on one page. No paywall, no signup.