Start with the Fundamentals layer and go in order. Each layer assumes the ones before it, and every topic links straight into the visualizer so you can practice as you read.

Do I need the math first?

Enough to follow along, not a degree. Linear Algebra and Statistics give you the vocabulary; the visualizer shows you what the equations are describing.

How do the topics connect to Confluence?

Every layer lists the exact visualizer tools and algorithms to open next — for example, Optimization pairs with the Training Playground, and Evaluation pairs with the confusion matrix and ROC views.

Are these links really free?

Everything listed here is freely accessible: open docs, free courses, public lectures, and open-access books and papers.

Learning roadmap

Learn the ideas behind the visuals.

Seven layers, from linear algebra to deployment. Each one pairs readings and videos with the exact visualizer tool to practice on.

7 layers · 67 curated links · 5 formats

Last updated · September 2026

7 of 7

Vectors, matrices, and transformations are the language of ML. Every model internally uses linear algebra for computation.
Vectors & MatricesEigenvalues & EigenvectorsMatrix DecompositionDot ProductsMatrix Inversion
Practice in Confluence
PCA Explorer — see eigenvectors in action
Related algorithms
pca lda linear-regression
Documentation
Math for Machine Learning Book NumPy Documentation
Videos
3Blue1Brown — Essence of Linear Algebra MIT 18.06 — Gilbert Strang Khan Academy — Linear Algebra
Courses
MIT OpenCourseWare 18.06
Books
Introduction to Linear Algebra — Gilbert Strang Mathematics for Machine Learning

Essential reading

Four texts worth your time.

The highest-signal books and journals we know — each link verified live before it earned a place here.

Keep learning

Recommended Learning Platforms.

Curated collection of documentation, interactive courses, and YouTube channels for continuous learning in machine learning and data science.

Documentation

scikit-learn Documentation PyTorch Documentation TensorFlow Documentation NumPy Documentation Pandas Documentation

YouTube channels

3Blue1Brown StatQuest with Josh Starmer MIT OpenCourseWare Stanford Online DeepLearning.AI Krish Naik CampusX freeCodeCamp.org Harvard University

FAQ

Questions, answered.

Start with the Fundamentals layer and go in order. Each layer assumes the ones before it, and every topic links straight into the visualizer so you can practice as you read.

Reading is half of it. Run the other half.

Every layer above maps to a tool waiting in the visualizer.

Open the visualizer