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Pre-MIT 6.S191: Deep Learning Bootcamp Prep
Get bootcamp-ready: the Python, math, and intuition you need to keep up with MIT's intensive 1-week Deep Learning Bootcamp.
~20 hours8 modulesVerifiable certificate$29 USD
The honest gap
6.S191 compresses a semester of deep learning into one week of lectures and labs. The lectures are world-class — but the labs assume you already write Python, understand gradient descent, and have used TensorFlow or PyTorch at least once. This course gets you there.
Who this is for
- Engineers and students attending or auditing MIT 6.S191
- Career switchers preparing for an intensive AI bootcamp
- Hiring managers who want to follow what their team is building
- Anyone wanting a stronger foundation before our CLLME diploma
Learning outcomes
- Be able to follow every line of code in the 6.S191 lab notebooks
- Train and debug a CNN, RNN, and small transformer end-to-end
- Diagnose underfitting vs overfitting from training curves
- Reproduce a paper's headline result from its abstract
- Earn a verifiable Pre-MIT 6.S191 Readiness Certificate
Full curriculum
8 modules · ~20 hours total
- 120 min
Module 1
Python for ML engineers (not for beginners)
Tighten the Python patterns ML code actually uses.
- Type hints, dataclasses, and clean module layout
- Numpy, pandas, and matplotlib in 60 minutes
- Jupyter / Colab survival kit
- 150 min
Module 2
Linear algebra and calculus, ML-flavoured
The math 6.S191 lecturers say out loud but rarely re-derive.
- Matrix calculus: shapes of every gradient you'll meet
- Eigenvalues, singular values, and why they matter for stability
- Probability and information theory cheat sheet
- 180 min
Module 3
Deep learning from first principles
Build the smallest possible neural network from scratch, then scale up.
- A 30-line NumPy network with manual backprop
- Re-implementing the same network in PyTorch and TensorFlow
- Why frameworks chose static vs dynamic graphs
- 180 min
Module 4
Computer vision warm-up — CNNs
Cover everything 6.S191's CNN lab assumes you already know.
- Convolutions, strides, padding — drawn
- Pooling and feature hierarchies
- Image classification on CIFAR-10 in PyTorch
- Transfer learning with a pretrained ResNet
- 200 min
Module 5
Sequence models — RNNs, LSTMs, and attention
Walk into the 6.S191 sequence lab understanding every block.
- Why sequence ≠ image
- RNN/LSTM/GRU side-by-side
- Attention mechanism explained twice (math + analogy)
- Mini transformer — 60 lines of PyTorch
- 120 min
Module 6
Generative models survey
Preview the 6.S191 deep generative lecture so you're ahead of it.
- Autoencoders → VAEs intuition
- GANs and why they're tricky to train
- Diffusion models — the one-paragraph version
- 150 min
Module 7
Reinforcement learning preview
Be ready for the 6.S191 RL lab — no surprises.
- MDPs, rewards, and the Bellman equation
- Policy gradients vs value-based methods
- Cartpole solved in 80 lines
- 200 min
Module 8
Capstone: reproduce a published result
Pick a small paper and reproduce its headline number — the skill 6.S191 rewards.
- How to read an ML paper in 20 minutes
- Setting up a reproducible experiment
- Writing a 2-page reproduction report (same format MIT teaches)
What you receive
- Lifetime access to all 8 modules + the capstone rubric
- Pre-MIT 6.S191 Readiness Certificate (PDF, verifiable)
- $100 discount code into the full CLLME LLM Engineering Diploma
- 1-page 'bootcamp-week' study plan timed to the official 6.S191 schedule
$29USD · one-time · lifetime access
Enroll NowUpgrade path
Ready to go deeper than a 1-week bootcamp? Build production LLMs in 12 modules.
Complete this prep and get $100 off the full Certified LLM Engineer (CLLME) diploma — 12 modules, capstone, 50-question exam, verifiable diploma. Your prep certificate counts toward Module 1.
See the full CLLME diplomaAuthoritative sources
Independent primary sources used to check and corroborate the guidance on this page.
NIST National Initiative for Cybersecurity Education (NICE)US national framework for cybersecurity work roles and competencies.
Financial Action Task Force (FATF)Global AML/CFT standards referenced by financial-crime curricula.
FBI Internet Crime Complaint Center — Annual ReportsPrimary loss and typology data used in fraud-investigation training.
US Federal Trade Commission — Consumer Sentinel Data BookOfficial complaint statistics for teaching fraud trend analysis.