A 36-week, milestone-driven journey that takes you from solid programming and math foundations all the way to deploying, monitoring, and specializing advanced machine-learning systems in production.

Python 3 Documentation

freeCodeCamp – Python for Beginners

Kaggle – Python Course

Python Programming Fundamentals

Pro Git Book & Docs

freeCodeCamp – Git & GitHub Tutorial

Atlassian Git Tutorials

Version Control with Git

Programming Foundations with Python & Git

Khan Academy – Linear Algebra

3Blue1Brown – Linear Algebra Playlist

Coursera – Mathematics for Machine Learning

Linear Algebra Essentials

Khan Academy – Statistics & Probability

StatQuest with Josh Starmer – Statistics Playlist

Think Stats (Book)

Probability & Statistics Basics

Math Foundations for Machine Learning

Pandas Documentation

Kaggle – Pandas Course

YouTube – Pandas Tutorial

Data Manipulation with Pandas

Matplotlib Documentation

Seaborn Documentation

freeCodeCamp – Data Visualization Tutorials

Data Visualization Fundamentals

Data Handling & Exploratory Data Analysis

Scikit-Learn User Guide

Andrew Ng – Machine Learning Course

Kaggle – Intro to Machine Learning

Supervised Learning

Scikit-Learn – Clustering & Dimensionality Reduction

YouTube – Unsupervised Learning Tutorial

Hands-On ML (Book)

Unsupervised Learning

Scikit-Learn – Model Selection & CV

Kaggle – Intermediate Machine Learning

Google – Feature Engineering Best Practices

Feature Engineering & Model Selection

Core Machine Learning Algorithms

DeepLearning.ai – Deep Learning Specialization

TensorFlow Tutorials

3Blue1Brown – Neural Networks Playlist

Neural Network Fundamentals

PyTorch Documentation

Kaggle – Deep Learning with PyTorch

YouTube – PyTorch Beginner Tutorial

Building Models with PyTorch

Stanford CS231n Notes

Coursera – Convolutional Neural Networks

YouTube – RNN/LSTM Tutorial

CNNs & RNNs

Deep Learning Foundations

MLflow Documentation

Weights & Biases Docs

YouTube – MLflow Tutorial

Experiment Design & Tracking

Optuna Documentation

Google – Hyperparameter Tuning Guide

Amazon – AutoML Books

Hyperparameter Optimization & AutoML

Model Evaluation & Experimentation

FastAPI Documentation

Docker Documentation

YouTube – Deploy ML Model with FastAPI

Model Packaging & API Deployment

Kubernetes Documentation

Jenkins Documentation

Google – MLOps Pipeline Tutorial

CI/CD & Container Orchestration

EvidentlyAI Docs

Coursera – MLOps Specialization

YouTube – ML Model Monitoring

Monitoring, Data Drift & Model Retraining

ML Engineering & MLOps

Hugging Face Transformers Documentation

Coursera – NLP Specialization

YouTube – Transformers Tutorial

Natural Language Processing

OpenCV Documentation

fast.ai – Practical Deep Learning for Coders (Vision)

YouTube – Object Detection Tutorial

Computer Vision

OpenAI Cookbook (Examples & Guides)

Google – Generative AI Crash Course

YouTube – Diffusion Models Tutorial

Generative AI & Large Language Models

Specialization Pathways

AI & Machine Learning Engineer Roadmap

AI & Machine Learning Engineer Roadmap

Journey Overview

Programming Foundations with Python & Git

Math Foundations for Machine Learning

Data Handling & Exploratory Data Analysis

Core Machine Learning Algorithms

Deep Learning Foundations

Model Evaluation & Experimentation

ML Engineering & MLOps

Specialization Pathways