A hands-on, developer-first introduction to Machine Learning where you implement core algorithms from scratch, validate them against scikit-learn, and build the foundation to confidently follow advanced topics like Reinforcement Learning, Diffusion Models, and 3D Reconstruction.
No black boxes: understand what ML libraries do by building the core algorithms yourself.
Developer mindset: focus on implementation, debugging, and evaluation.
Future-proof foundation: this is the prerequisite course for almost any modern ML topic.
Designed for devs: clear modules, runnable code, and practical evaluation (train/val/test, overfitting, cross-validation).
Most people get stuck because modern Machine Learning topics assume you already have the fundamentals. I’ve taught advanced ML concepts online, at conferences, and inside companies—and I built this course to close that gap. After this course, you’ll have the foundation to follow my advanced courses and build the skills that top teams hire for, often in the low-to-mid six figures.
You will write clean implementations step-by-step, then benchmark them against scikit-learn.
As a developer, you don’t gain confidence by memorizing definitions—you gain it by reading and writing code. Once you’ve built the internals yourself, libraries stop being black boxes and you can:
If you want a “click a button and get a model” tutorial only using high-level APIs, this won’t be a fit. This course is for devs who want real understanding and the ability to implement and reason about ML systems.
You don’t need a math degree—just enough comfort with matrices and derivatives.
A practical roadmap that starts with simple models and ends with neural networks. You’ll also see how this connects to ChatGPT training and generative models at a conceptual level.
AI vs ML vs DL, hyperparameters, overfitting, train/val/test, and K-fold cross-validation (implemented).
KNN, linear & logistic regression, SVMs, decision trees — then compare with scikit-learn.
Forward pass, activations, loss, training loop, backprop, optimizer, initialization, results.
Unsupervised learning + a guided mental map toward RL and generative models (GANs / diffusion).
You’ll be able to read an ML notebook or paper and understand what’s happening at the level that matters: data, objective, optimization, evaluation, and implementation details.
I’ve had the privilege of teaching advanced machine learning concepts to thousands of learners worldwide. Students often highlight the clarity and structure of my teaching approach.
“I started this course after spending a long time struggling to understand how to implement a diffusion model. The explanations here are very clear and thorough! This course saves a lot of time, and makes things clear that other websites/tutorials don't cover properly. I recommend this class!”
— Daniel E.
“I'm extremely happy to have found this course. As I have a math background but no specific ML experience beyond generalities this is the perfect way to get deeper, hands-on knowledge.”
— Matthew C.
If you’re a developer and you want a real ML foundation—this is the course that turns “I used sklearn once” into “I understand what’s happening.”
You need high-school algebra and a practical understanding of derivatives. The course focuses on implementation and intuition, not proof-heavy theory.
You implement the core algorithms from scratch first, then compare to scikit-learn to understand performance, evaluation, and what libraries do under the hood.
Yes—this is the whole point of the course. Once you understand optimization, losses, evaluation, and neural nets at an implementation level, advanced topics stop feeling like a wall of jargon.
You’ll be able to build ML features with confidence: you’ll know how to pick models, validate results, and debug training behavior because you understand the internals.