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AI Engineering from Scratch 1: Foundations
Build the foundation every AI engineer needs — a professional tooling setup, the math that powers neural networks, classical machine learning, and deep learning from the first perceptron to your own mini framework.
Curriculum
Setup & Tooling
- Dev EnvironmentPreview45 min
- Git & CollaborationPreview30 min
- GPU Setup & Cloud45 min
- APIs & Keys30 min
- Jupyter Notebooks30 min
- Python Environments30 min
- Docker for AI60 min
- Editor Setup20 min
- Data Management45 min
- Terminal & Shell35 min
- Linux for AI30 min
- Debugging and Profiling60 min
- Checkpoint: Setup & Tooling5 min
Math Foundations
- Linear Algebra Intuition60 min
- Vectors, Matrices & Operations60 min
- Matrix Transformations75 min
- Calculus for Machine Learning60 min
- Chain Rule & Automatic Differentiation90 min
- Probability and Distributions75 min
- Bayes' Theorem75 min
- Optimization75 min
- Information Theory60 min
- Dimensionality Reduction90 min
- Singular Value Decomposition120 min
- Tensor Operations90 min
- Numerical Stability120 min
- Norms and Distances90 min
- Statistics for Machine Learning120 min
- Sampling Methods120 min
- Linear Systems120 min
- Convex Optimization90 min
- Complex Numbers for AI60 min
- The Fourier Transform90 min
- Graph Theory for Machine Learning90 min
- Stochastic Processes75 min
- Checkpoint: Math Foundations5 min
ML Fundamentals
- What Is Machine Learning45 min
- Linear Regression90 min
- Logistic Regression90 min
- Decision Trees and Random Forests90 min
- Support Vector Machines90 min
- K-Nearest Neighbors and Distances90 min
- Unsupervised Learning90 min
- Feature Engineering & Selection90 min
- Model Evaluation90 min
- Bias-Variance Tradeoff75 min
- Ensemble Methods120 min
- Hyperparameter Tuning90 min
- ML Pipelines120 min
- Naive Bayes75 min
- Time Series Fundamentals90 min
- Anomaly Detection75 min
- Handling Imbalanced Data90 min
- Feature Selection75 min
- Checkpoint: ML Fundamentals5 min
Deep Learning Core
- The Perceptron60 min
- Multi-Layer Networks and Forward Pass90 min
- Backpropagation from Scratch120 min
- Activation Functions75 min
- Loss Functions75 min
- Optimizers75 min
- Regularization75 min
- Weight Initialization and Training Stability90 min
- Learning Rate Schedules and Warmup90 min
- Build Your Own Mini Framework120 min
- Introduction to PyTorch75 min
- Introduction to JAX90 min
- Debugging Neural Networks90 min
- Checkpoint: Deep Learning Core5 min