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ai-engineering-from-scratch

Learn it. Build it. Ship it for others.

agentsaiai-agentsai-engineeringcomputer-visioncoursedeep-learningfrom-scratchmcppython

Description

Learn it. Build it. Ship it for others.

README

💬 "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-levelcheck-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

Phase 0Phase 1Phase 2Phase 3Phase 4Phase 5Phase 6Phase 7Phase 8Phase 9Phase 10Phase 11Phase 12Phase 13Phase 14Phase 15Phase 16Phase 17Phase 18Phase 19Legend: BuildLearn

🛠️ Get your environment ready for everything that follows.

# Lesson Type Lang
01 Dev Environment Build02 Git & Collaboration Learn03 GPU Setup & Cloud Build04 APIs & Keys Build05 Jupyter Notebooks Build06 Python Environments Build07 Docker for AI Build08 Editor Setup Build09 Data Management Build10 Terminal & Shell Learn11 Linux for AI Learn12 Debugging & Profiling Build 🟣 Phase 1 — Math Foundations  22 lessons  The intuition behind every AI algorithm, through code.
# Lesson Type Lang
01 Linear Algebra Intuition Learn02 Vectors, Matrices & Operations Build03 Matrix Transformations & Eigenvalues Build04 Calculus for ML: Derivatives & Gradients Learn05 Chain Rule & Automatic Differentiation Build06 Probability & Distributions Learn07 Bayes' Theorem & Statistical Thinking Build08 Optimization: Gradient Descent Family Build09 Information Theory: Entropy, KL Divergence Learn10 Dimensionality Reduction: PCA, t-SNE, UMAP Build11 Singular Value Decomposition Build12 Tensor Operations Build13 Numerical Stability Build14 Norms & Distances Build15 Statistics for ML Build16 Sampling Methods Build17 Linear Systems Build18 Convex Optimization Build19 Complex Numbers for AI Learn20 The Fourier Transform Build21 Graph Theory for ML Build22 Stochastic Processes Learn 🔵 Phase 2 — ML Fundamentals  18 lessons  Classical ML — still the backbone of most production AI.
# Lesson Type Lang
01 What Is Machine Learning Learn02 Linear Regression from Scratch Build03 Logistic Regression & Classification Build04 Decision Trees & Random Forests Build05 Support Vector Machines Build06 KNN & Distance Metrics Build07 Unsupervised Learning: K-Means, DBSCAN Build08 Feature Engineering & Selection Build09 Model Evaluation: Metrics, Cross-Validation Build10 Bias, Variance & the Learning Curve Learn11 Ensemble Methods: Boosting, Bagging, Stacking Build12 Hyperparameter Tuning Build13 ML Pipelines & Experiment Tracking Build14 Naive Bayes Build15 Time Series Fundamentals Build16 Anomaly Detection Build17 Handling Imbalanced Data Build18 Feature Selection Build 🟢 Phase 3 — Deep Learning Core  13 lessons  Neural networks from first principles. No frameworks until you build one.
# Lesson Type Lang
01 The Perceptron: Where It All Started Build02 Multi-Layer Networks & Forward Pass Build03 Backpropagation from Scratch Build04 Activation Functions: ReLU, Sigmoid, GELU & Why Build05 Loss Functions: MSE, Cross-Entropy, Contrastive Build06 Optimizers: SGD, Momentum, Adam, AdamW Build07 Regularization: Dropout, Weight Decay, BatchNorm Build08 Weight Initialization & Training Stability Build09 Learning Rate Schedules & Warmup Build10 Build Your Own Mini Framework Build11 Introduction to PyTorch Build12 Introduction to JAX Build13 Debugging Neural Networks Build 🟠 Phase 4 — Computer Vision  28 lessons  From pixels to understanding — image, video, 3D, VLMs, and world models.
# Lesson Type Lang
01 Image Fundamentals: Pixels, Channels, Color Spaces Learn02 Convolutions from Scratch Build03 CNNs: LeNet to ResNet Build04 Image Classification Build05 Transfer Learning & Fine-Tuning Build06 Object Detection — YOLO from Scratch Build07 Semantic Segmentation — U-Net Build08 Instance Segmentation — Mask R-CNN Build09 Image Generation — GANs Build10 Image Generation — Diffusion Models Build11 Stable Diffusion — Architecture & Fine-Tuning Release History
VersionChangesUrgencyDate
0.0.0No release found — using repo HEADHigh4/21/2026

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