Machine learning is best learned as an experimental discipline. Build baselines, measure carefully, inspect failures, and improve the data before reaching for more complex models.
- 01
Learn Python and data
Become comfortable with arrays, dataframes, visualization, notebooks, version control, and writing reproducible experiments.
- 02
Master the core models
Study regression, trees, clustering, feature engineering, validation, metrics, and the tradeoffs between bias and variance.
- 03
Add deep learning
Learn tensors, optimization, neural networks, attention, and how to diagnose training rather than treating models as black boxes.
- 04
Ship and monitor
Package inference, track experiments, test data pipelines, monitor drift, and connect model quality to product outcomes.