Everyone in the world should be able to learn computer science, for free. We're starting with machine learning, taking you from zero to hero, one concept at a time.
📺 YouTube: youtube.com/@mlzerotoherousa: a new video every 12 hours, 8 AM and 8 PM Pacific.
🧠 intuition first → 🎞️ animated graphics → 🔢 a tiny worked example → ∑ the math, symbol by symbol → ✅ a quick check
mindmap
root((Zero → Hero))
1 · Zero: Math
Linear algebra
Vectors and matrices
Eigenvalues, SVD
Calculus
Derivatives, chain rule
Gradients, Jacobian, Hessian
Probability and statistics
2 · Basics: Core ML
ML foundations
Linear and logistic regression
Gradient descent
Bias-variance tradeoff
Data and features
Evaluation and metrics
Classical models
Trees, SVMs, ensembles
Unsupervised learning
Clustering, PCA
3 · Intermediate: Deep learning
Neural networks
Backpropagation
Optimizers, regularization
Computer vision
CNNs
Sequences and NLP
RNNs, embeddings
4 · Advanced: Modern AI
Transformers
Self-attention
Large language models
Pretraining, fine-tuning
RAG, agents
Generative models
VAEs, GANs, diffusion
Reinforcement learning
5 · Pro: Hero
Probabilistic and causal ML
Learning theory
Interpretability and safety
MLOps and systems
Research and career
Each video has a folder with runnable Python and the notes behind it:
| Topic | Repo |
|---|---|
| Math foundations (linear algebra, calculus, probability, optimization) | math-foundations |
| ML foundations | ml-foundations |
| Classical supervised learning | classical-supervised-learning |
| Deep learning foundations | deep-learning-foundations |
| Computer vision | computer-vision |
| Transformers | transformers |
| Large language models | large-language-models |
| Generative models | generative-models |
Found a mistake in a video or its code? Open an issue on the topic repo above.
Made in San Francisco. Topic requests welcome: open an issue.