Finguard courses

Learn AI the way it actually works.

Interactive tracks, built around figures you can touch rather than walls of text. Start with the fundamentals, prove them on real projects, then go deep — from the models behind ChatGPT and Claude to machine learning for finance. Free, no account, runs in your browser.

The catalog

Pick your track.

Beginner → Advanced

Finguard ML

Machine learning, foundations to frontier

The complete course: from "what is a model" through linear and logistic regression, trees and ensembles, neural networks, CNNs, transformers, and production ML. Every concept is a live, interactive figure.

41 units334 lessons9 sections
Not started yet
Applied

Applied ML: Projects

Five end-to-end builds on real data

Put the theory to work. A complete ML roadmap followed by hands-on projects on real datasets — tabular, text, images, time series, and recommenders — each taken from raw data to a working model.

6 units5 projectsbuilds on Finguard ML
Part of the Finguard ML track
Advanced

Finguard LLMs

Large language models, end to end

How ChatGPT and Claude actually work. Next-token prediction, tokenization, the Transformer (with a live attention matrix you drive), training at scale, and alignment — RLHF and Constitutional AI. The deep, technical tier.

33 units7 sectionsassumes ML basics
Not started yet
New
Practical

Finguard AI Engineering

Build software with LLMs

The production track: prompting and structured outputs, retrieval (RAG), agents and tools, evaluation and observability, guardrails, and the cost and latency work that ships an AI feature.

12 sections~60 unitsfor engineers
Sections 1-2 live now
New
Applied

Finguard ML Finance

Machine learning for financial services

Credit scoring, fraud detection, market risk and Value-at-Risk, financial time-series, and anti-money-laundering — plus two fintech case studies. Real public datasets, real in-browser math.

7 units75 lessons2 sections
Not started yet
Zero → Advanced

Test Yourself

Auto-graded Python Code Lab

Stop reading, start typing. Fill-in-the-blank Python exercises with pandas and scikit-learn running in your browser — from your first print() to gradient descent and k-means from scratch, each checked the instant you run it.

5 levels25 exercisesauto-graded
Not started yet
Coming soon

Finguard GenAI

Diffusion & multimodal generation

Image, video, and audio generation: VAEs and GANs, diffusion and score-based models, latent diffusion and text-to-image, and how multimodal models see.

in development
On the roadmap
Coming soon

Reinforcement Learning & Agents

From bandits to deep RL

MDPs, dynamic programming, Q-learning, policy gradients and deep RL (DQN, PPO), the link to RLHF, and game-playing and agentic systems — with interactive gridworlds.

in development
On the roadmap
Coming soon

Mathematics for ML

The foundations, made visual

Linear algebra, calculus and gradients, probability and statistics, optimization, and information theory — the math every other course assumes, in a format built for it.

in development
On the roadmap
A suggested path

Where to start.

New to the field or coming back for the hard parts — here's the order we'd take them in.

Step 01
Learn the fundamentals
Work through Finguard ML. Build real intuition for models, training, and evaluation — everything later courses assume.
Step 02
Prove it on projects
Apply the theory end-to-end on real datasets. This is where the concepts turn into skills you can show.
Step 03
Go deep on LLMs
With the basics solid, take the advanced LLMs track and understand the systems behind modern AI assistants.
Begin

Open any course in one click.

No account, no install. Your progress saves automatically in your browser, separately for each course.