Math, Tooling, and Classical Machine Learning
- Dev Environment
- Git & Collaboration
- GPU Setup & Cloud
- APIs & Keys
- Jupyter Notebooks
- Python Environments
- Docker for AI
- Editor Setup
- Data Management
- Terminal & Shell
- Linux for AI
- Debugging & Profiling
- Python for AI Engineering
- Linear Algebra Intuition
- Vectors, Matrices & Operations
- Matrix Transformations & Eigenvalues
- Calculus for ML: Derivatives & Gradients
- Chain Rule & Automatic Differentiation
- Probability & Distributions
- Bayes' Theorem & Statistical Thinking
- Optimization: Gradient Descent Family
- Information Theory: Entropy, KL Divergence
- Dimensionality Reduction: PCA, t-SNE, UMAP
- Singular Value Decomposition
- Tensor Operations
- Numerical Stability
- Norms & Distances
- Statistics for ML
- Sampling Methods
- Linear Systems
- Convex Optimization
- Complex Numbers for AI
- The Fourier Transform
- Graph Theory for ML
- Stochastic Processes
- What Is Machine Learning
- Linear Regression from Scratch
- Logistic Regression & Classification
- Decision Trees & Random Forests
- Support Vector Machines
- KNN & Distance Metrics
- Unsupervised Learning: K-Means, DBSCAN
- Feature Engineering & Selection
- Model Evaluation: Metrics, Cross-Validation
- Bias, Variance & the Learning Curve
- Ensemble Methods: Boosting, Bagging, Stacking
- Hyperparameter Tuning
- ML Pipelines & Experiment Tracking
- Naive Bayes
- Time Series Fundamentals
- Anomaly Detection
- Handling Imbalanced Data
- Feature Selection
- ML System Design — From Problem to Deployed Model
- Monitoring Models in Production — Data Drift and Concept Drift
- Feature Stores and Training-Serving Skew