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ai-engineering-from-scratch

Learn it. Build it. Ship it for others.

agents ai ai-agents ai-engineering computer-vision course deep-learning from-scratch mcp python

Why this rank:Strong adoptionRecent releaseHealthy release cadence

Description

Learn it. Build it. Ship it for others.

README

🧠 AI Engineering from Scratch

From linear algebra to autonomous agent swarms. learn AI with AI, then ship the tools.

🧭 Quick Navigation

🚀 Get Started · 🤖 AI-Native · 🗺️ The Journey · 🧰 Toolkit · 📚 Glossary · 🛣️ Roadmap · 🤝 Contribute · 🌐 Website

💬 "84% of students already use AI tools. Only 18% feel prepared to use them professionally.

This course closes that gap."

272+ lessons. 20 phases. ~306 hours. From linear algebra to autonomous agent swarms. Python, TypeScript, Rust, Julia. Every lesson produces something reusable: prompts, skills, agents, and MCP servers.

You don't just learn AI. You learn AI with AI. Then you build real things. Then you ship tools others can use.

🆚 Why This Course?

📺 Traditional Courses	🧠 This Course
Scope One slice (NLP or Vision or Agents)	Scope 🌍 Everything — math · ML · DL · NLP · vision · speech · transformers · LLMs · agents · swarms
Languages Python only	Languages 🐍 Python · 🟦 TypeScript · 🦀 Rust · 🟣 Julia
Output "I learned something"	Output 📦 A portfolio of tools, prompts, skills, and agents you can install
Depth Surface-level or theory-heavy	Depth 🔬 Build from scratch first, then use frameworks
Format Videos you watch	Format 💻 Runnable code + docs + web app + AI-powered quizzes
Style Passive consumption	Style 🤖 AI-native — Claude Code skills test you as you go

🤖 AI-Native Learning

This isn't a course you watch. It's a course you use with your AI coding agent.

🎯 Learn with AI, not just about AI

# 🧪 Find where to start based on what you already know
/find-your-level

# ✅ Quiz yourself after completing a phase
/check-understanding 3

# 📦 Every lesson produces a reusable artifact
ls phases/03-deep-learning-core/05-loss-functions/outputs/
# ├── prompt-loss-function-selector.md
# └── prompt-loss-debugger.md

🛠️ Built-in Claude Code Skills

🎴 Skill ⚡ What it does

find-your-level

check-understanding

🚢 Every Lesson Ships Something

Other courses end with "congratulations, you learned X." Our lessons end with a reusable tool:

📝
Prompts
_{Paste into any AI assistant for expert-level help}

🎴
Skills
_{Install into Claude Code, Cursor, or any agent}

🤖
Agents
_{Deploy as autonomous workers}

🔌
MCP Servers
_{Plug into any MCP-compatible AI app}

277-term searchable glossary. Full lesson catalog. ~306 hours of content with per-lesson time estimates.
🌐 Browse the website →

🗺️ The Journey

20 phases · 272+ lessons · click any phase to expand

🛠️ Get your environment ready for everything that follows.

# Lesson Type Lang

01

Dev Environment

Build

Git & Collaboration

Learn

GPU Setup & Cloud

Build

Build

Jupyter Notebooks

Build

Python Environments

Build

Build

Build

Data Management

Build

Terminal & Shell

Learn

Learn

Debugging & Profiling

Build

🟣 Phase 1 — Math Foundations 22 lessons The intuition behind every AI algorithm, through code.

# Lesson Type Lang

Linear Algebra Intuition

Learn

Vectors, Matrices & Operations

Build

Matrix Transformations & Eigenvalues

Build

Calculus for ML: Derivatives & Gradients

Learn

Chain Rule & Automatic Differentiation

Build

Probability & Distributions

Learn

Bayes' Theorem & Statistical Thinking

Build

Optimization: Gradient Descent Family

Build

Information Theory: Entropy, KL Divergence

Learn

Dimensionality Reduction: PCA, t-SNE, UMAP

Build

Singular Value Decomposition

Build

Tensor Operations

Build

Numerical Stability

Build

Norms & Distances

Build

Statistics for ML

Build

Sampling Methods

Build

Build

Convex Optimization

Build

Complex Numbers for AI

Learn

The Fourier Transform

Build

Graph Theory for ML

Build

Stochastic Processes

Learn

🔵 Phase 2 — ML Fundamentals 18 lessons Classical ML — still the backbone of most production AI.

# Lesson Type Lang

What Is Machine Learning

Learn

Linear Regression from Scratch

Build

Logistic Regression & Classification

Build

Decision Trees & Random Forests

Build

Support Vector Machines

Build

KNN & Distance Metrics

Build

Unsupervised Learning: K-Means, DBSCAN

Build

Feature Engineering & Selection

Build

Model Evaluation: Metrics, Cross-Validation

Build

Bias, Variance & the Learning Curve

Learn

Ensemble Methods: Boosting, Bagging, Stacking

Build

Hyperparameter Tuning

Build

ML Pipelines & Experiment Tracking

Build

Build

Time Series Fundamentals

Build

Anomaly Detection

Build

Handling Imbalanced Data

Build

Feature Selection

Build

🟢 Phase 3 — Deep Learning Core 13 lessons Neural networks from first principles. No frameworks until you build one.

# Lesson Type Lang

The Perceptron: Where It All Started

Build

Multi-Layer Networks & Forward Pass

Build

Backpropagation from Scratch

Build

Activation Functions: ReLU, Sigmoid, GELU & Why

Build

Loss Functions: MSE, Cross-Entropy, Contrastive

Build

Optimizers: SGD, Momentum, Adam, AdamW

Build

Regularization: Dropout, Weight Decay, BatchNorm

Build

Weight Initialization & Training Stability

Build

Learning Rate Schedules & Warmup

Build

Build Your Own Mini Framework

Build

Introduction to PyTorch

Build

Introduction to JAX

Build

Debugging Neural Networks

Build

🟠 Phase 4 — Computer Vision 28 lessons From pixels to understanding — image, video, 3D, VLMs, and world models.

Image Fundamentals: Pixels, Channels, Color Spaces

Learn

Convolutions from Scratch

Build

CNNs: LeNet to ResNet

Build

Image Classification

Build

Transfer Learning & Fine-Tuning

Build

Object Detection — YOLO from Scratch

Build

Semantic Segmentation — U-Net

Build

Instance Segmentation — Mask R-CNN

Build

Image Generation — GANs

Build

Image Generation — Diffusion Models

Build

Stable Diffusion — Architecture & Fine-Tuning

Release History

Version	Changes	Urgency	Date
main@2026-06-03	Latest activity on main branch	High	6/3/2026
0.0.0	No release found — using repo HEAD	High	4/21/2026

Dependencies & License Audit

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