DLH — Machine Learning
A structured curriculum building the mathematical and data engineering foundations for machine learning.
Directory Structure
dlh-machine_learning/
├── math/ # Mathematical foundations
│ ├── linear_algebra/ # Matrix operations: Python lists → NumPy
│ │ ├── 0-slice_me_up.py through 14-saddle_up.py
│ │ ├── 100-slice_like_a_ninja.py through 102-squashed_like_sardines.py
│ │ └── README.md
│ └── README.md
├── pipeline/ # Data engineering
│ ├── databases/ # SQL: creation, CRUD, joins, aggregates, triggers
│ │ ├── 0-create_database_if_missing.sql through 18-valid_email.sql
│ │ ├── hbtn_0d_tvshows.sql, hbtn_0d_tvshows_rate.sql
│ │ ├── metal_bands.sql, temperatures.sql
│ │ └── README.md
│ └── README.md
├── my-venv/ # Python virtual environment
└── README.md
Quick Reference
| Track | Module | Topics | Tasks |
|---|---|---|---|
| Math | Linear Algebra | Slicing, shape, transpose, element-wise ops, concat, matrix multiply, NumPy, n-D generalization | 19 |
| Pipeline | Databases | DDL, CRUD, WHERE, ORDER BY, GROUP BY, JOINS, aggregates, constraints, triggers | 18 (+4 schemas) |
Learning Progression
Math Track
- Python Slicing → 2. Manual Matrix Ops (nested loops) → 3. NumPy Vectorization → 4. N-Dimensional Generalization
Pipeline Track
- Foundation (CREATE) → 2. CRUD → 3. Filtering/Sorting → 4. Joins → 5. Constraints → 6. Real-World Data → 7. Triggers
Setup
cd dlh-machine_learning
source my-venv/bin/activate
pip install numpy