A structured path from setting up Python to deploying production models. Every equation derived. Every algorithm implemented from scratch. No black boxes, no paywalls.
The Path
Nine modules, each building on the last. Start at zero, finish with a deployed ML system.
Python, Conda, NumPy, Pandas, Matplotlib, Seaborn, and full EDA.
Linear algebra, calculus & gradient descent, probability & statistics.
Data cleaning, scaling, categorical encoding, feature selection & PCA.
Regression, classification, SVM, decision trees, Random Forest, XGBoost.
K-Means, hierarchical clustering, DBSCAN, t-SNE, LDA, and association rules.
Bias–variance, confusion matrix, ROC-AUC, regression metrics, Optuna tuning.
Perceptrons, MLP with Keras, backprop, Adam, CNNs, LSTMs, transfer learning.
Agent–environment loop, Q-Learning from scratch, and DQN on CartPole.
Joblib persistence, FastAPI, Docker, GitHub Actions CI/CD, Streamlit, AWS, Render.
Every algorithm is derived from its mathematical roots. KaTeX-rendered equations with every symbol explained. Intuition first, proof second.
Every algorithm implemented in raw NumPy first, then scikit-learn, then production libraries. You understand the code because you built the foundation.
The curriculum ends where careers begin: FastAPI, Docker, GitHub Actions CI/CD, and cloud deployment to AWS, Render, and Streamlit.
Reference Guides
Three standalone reference pages that sit alongside the modules — for when you need a syntax lookup, a production architecture, or an MLOps tool decision.
Production
Ten production ML domains — Computer Vision, NLP/LLMs, Tabular ML, GenAI, Time Series, Recommendation Systems, and more — each with pipeline diagrams, folder structures, and artifact checkpoints.
Quick Reference
Syntax and worked examples for NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow/Keras — all in one place with copy-paste code blocks.
Infrastructure
The full MLOps stack — cloud providers, Docker, Kubernetes, data pipelines, experiment tracking, model serving, monitoring, LLMOps, hardware acceleration, and deployment guides for Django, Flask, and FastAPI.
The Ecosystem
Companion tools built alongside the curriculum — all browser-based, all free, no sign-up required.
Serverless Python IDE
Run Python with NumPy, Pandas, and Matplotlib entirely in your browser via Pyodide WebAssembly. Monaco editor, inline plots, package installer. Zero setup.
Interactive SVM Game
Master SVMs through gameplay — drag a hyperplane sword to separate data points, maximise margins, and switch kernels (Linear → RBF → Polynomial) across 5 levels, each teaching a real SVM concept.
Rich Text → Markdown
Convert rich text and HTML to clean Markdown instantly. Real-time preview, full formatting support, one-click copy, download as .md. Everything runs in your browser — nothing sent to any server.
Run lesson code live: Open PyOrbit IDE side-by-side with any lesson and
paste the code snippets directly — NumPy, Pandas, and Matplotlib are pre-loaded, no
pip install needed.
How It Works
Study the lesson — intuition first, math second, code third.
Paste code into PyOrbit IDE and execute it live in your browser.
Reinforce SVM intuition with Hyperplane Hero after Lesson 4.3.
Module 09 takes your model live — FastAPI, Docker, and AWS.
About
ZeroToML fills the gap between shallow tutorials and dense academic papers. Every concept is explained with intuition first, mathematics second, and working code third.
Created by Muhammad Waqas — a developer who learned ML the hard way and built the resource he wished had existed.