A study group’s knowledge base
Machine learning,
read slowly.
Avalon is our living curriculum — from Python and statistics to production ML. Written for each other, with the math in view.
8+Core Categories
LaTeXKaTeX Math
LiveCMS & MDX
⌘KInstant Search
The shelves
ML topic curriculum
Structured tracks from vector spaces to transformer fine-tuning.
The curriculum
Master roadmap
Six sequential phases — from Python and statistics to Scikit-Learn, deliberate practice, and a production project.
33%
complete
1.0
DoneLearn Python & Data Foundations
- Jupyter Notebook Setup
- Pandas Basics & Joins
- NumPy Vectorized Ops
- Plotting & Stats
2.0
DoneBasic Statistics & Probability
- Descriptive Statistics
- Probability Distributions
- Hypothesis Testing
- Z/t-tests & p-values
3.0
ActiveCore ML Concepts & Algorithms
- Linear & Logistic Regression
- Decision Trees & Ensembles
- Random Forests, Boosting & SVM
- ISLR Book Reference
4.0
ActiveScikit-Learn Mastery
- Scikit-Learn 1.4.2 Tutorials
- API Documentation
- Estimator Pipelines
- Evaluation Metrics
5.0
Next upPractice ML (The Genius Move)
- 1. Implement from Scratch
- 2. Implement in SKLearn (Toy)
- 3. Benchmark Custom vs SKLearn
6.0
UpcomingEnd-to-End Industry Project
- 8-Step Project Lifecycle
- EDA & Hypothesis Testing
- Feature Engineering
- Baselines to Ensemble Tuning
Contributors