@wsl8297: Want to systematically learn AI Engineering without jumping between papers, tutorials, and repos? Check out rohitg00/ai-engineering-from-scratch. This course has 10k+ stars, 435 lessons, 20 stages, covering from math foundations, neural networks...

X AI KOLs Timeline 05/22/26, 02:04 AM Tools

ai-engineering curriculum open-source github learning python typescript

Summary

Recommends an open-source GitHub repo called ai-engineering-from-scratch, containing 435 lessons across 20 stages, systematically covering a complete learning path for AI engineering from math foundations to Agents.

Want to systematically learn AI Engineering without jumping between papers, tutorials, and repos? Check out rohitg00/ai-engineering-from-scratch. This course already has 10k+ stars, 435 lessons, 20 stages, covering from math foundations, neural networks, RAG, agents, all the way to shipping things for others to use. Github: https://github.com/rohitg00/ai-engineering-from-scratch… What I like about it is the roadmap is long enough to serve as a map to gradually fill in gaps.

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If you want to systematically learn AI Engineering without jumping between papers, tutorials, and code repositories, check out rohitg00/ai-engineering-from-scratch. This course has already earned 10k+ stars, with 435 lessons across 20 phases, covering math fundamentals, neural networks, RAG, agents, all the way to shipping your work for others to use.
GitHub: https://github.com/rohitg00/ai-engineering-from-scratch…

What I like about it is that the roadmap is long enough—it works well as a map you can slowly fill in.

name: agent-loop
description: ReAct-style loop for any tool list
phase: 14
lesson: 01

Implement a minimal agent loop that…
``

outputs/prompt-debug-agent.md

markdown You are an agent debugger. Given the trace of an agent run, identify the step where the agent went wrong and explain why...

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Twenty phases. Click any phase to expand its lesson list.

Phase 0: Setup & Tooling — `12 lessons`

Get your environment ready for everything that follows.

#	Lesson	Type	Lang
01	Dev Environment	Build	Python, TypeScript, Rust
02	Git & Collaboration	Learn	—
03	GPU Setup & Cloud	Build	Python
04	APIs & Keys	Build	Python, TypeScript
05	Jupyter Notebooks	Build	Python
06	Python Environments	Build	Python
07	Docker for AI	Build	Python
08	Editor Setup	Build	—
09	Data Management	Build	Python
10	Terminal & Shell	Learn	—
11	Linux for AI	Learn	—
12	Debugging & Profiling	Build	Python

Phase 1 — Math Foundations — 22 lessons

The intuition behind every AI algorithm, through code.

#	Lesson	Type	Lang
01	Linear Algebra Intuition	Learn	Python, Julia
02	Vectors, Matrices & Operations	Build	Python, Julia
03	Matrix Transformations & Eigenvalues	Build	Python, Julia
04	Calculus for ML: Derivatives & Gradients	Learn	Python
05	Chain Rule & Automatic Differentiation	Build	Python
06	Probability & Distributions	Learn	Python
07	Bayes’ Theorem & Statistical Thinking	Build	Python
08	Optimization: Gradient Descent Family	Build	Python
09	Information Theory: Entropy, KL Divergence	Learn	Python
10	Dimensionality Reduction: PCA, t-SNE, UMAP	Build	Python
11	Singular Value Decomposition	Build	Python, Julia
12	Tensor Operations	Build	Python
13	Numerical Stability	Build	Python
14	Norms & Distances	Build	Python
15	Statistics for ML	Build	Python
16	Sampling Methods	Build	Python
17	Linear Systems	Build	Python
18	Convex Optimization	Build	Python
19	Complex Numbers for AI	Learn	Python
20	The Fourier Transform	Build	Python
21	Graph Theory for ML	Build	Python
22	Stochastic Processes	Learn	Python

Phase 2 — ML Fundamentals — 18 lessons

Classical ML — still the backbone of most production AI.

#	Lesson	Type	Lang
01	What Is Machine Learning	Learn	Python
02	Linear Regression from Scratch	Build	Python
03	Logistic Regression & Classification	Build	Python
04	Decision Trees & Random Forests	Build	Python
05	Support Vector Machines	Build	Python
06	KNN & Distance Metrics	Build	Python
07	Unsupervised Learning: K-Means, DBSCAN	Build	Python
08	Feature Engineering & Selection	Build	Python
09	Model Evaluation: Metrics, Cross-Validation	Build	Python
10	Bias, Variance & the Learning Curve	Learn	Python
11	Ensemble Methods: Boosting, Bagging, Stacking	Build	Python
12	Hyperparameter Tuning	Build	Python
13	ML Pipelines & Experiment Tracking	Build	Python
14	Naive Bayes	Build	Python
15	Time Series Fundamentals	Build	Python
16	Anomaly Detection	Build	Python
17	Handling Imbalanced Data	Build	Python
18	Feature Selection	Build	Python

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	Build	Python
02	Multi-Layer Networks & Forward Pass	Build	Python
03	Backpropagation from Scratch	Build	Python
04	Activation Functions: ReLU, Sigmoid, GELU & Why	Build	Python
05	Loss Functions: MSE, Cross-Entropy, Contrastive	Build	Python
06	Optimizers: SGD, Momentum, Adam, AdamW	Build	Python
07	Regularization: Dropout, Weight Decay, BatchNorm	Build	Python
08	Weight Initialization & Training Stability	Build	Python
09	Learning Rate Schedules & Warmup	Build	Python
10	Build Your Own Mini Framework	Build	Python
11	Introduction to PyTorch	Build	Python
12	Introduction to JAX	Build	Python
13	Debugging Neural Networks	Build	Python

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	Learn	Python
02	Convolutions from Scratch	Build	Python
03	CNNs: LeNet to ResNet	Build	Python
04	Image Classification	Build	Python
05	Transfer Learning & Fine-Tuning	Build	Python
06	Object Detection — YOLO from Scratch	Build	Python
07	Semantic Segmentation & U-Net	Build	Python
08	Image Augmentation & Datasets	Build	Python
09	Generative Models: GANs & VAEs	Build	Python
10	Diffusion Models for Images	Build	Python
11	Vision Transformers (ViT)	Build	Python
12	Object Tracking	Build	Python
13	Image Captioning	Build	Python
14	Visual Question Answering	Build	Python
15	3D Vision & Point Clouds	Build	Python
16	Video Understanding	Build	Python
17	Self-Supervised Vision	Build	Python
18	Vision-Language Models (CLIP)	Build	Python
19	Image Retrieval & Search	Build	Python
20	Neural Radiance Fields (NeRF)	Build	Python
21	Face Recognition & Verification	Build	Python
22	Optical Character Recognition	Build	Python
23	Medical Image Analysis	Build	Python
24	Image Super-Resolution	Build	Python
25	Action Recognition	Build	Python
26	Autonomous Driving Perception	Build	Python
27	Adversarial Attacks on Vision Models	Build	Python
28	World Models	Build	Python

@wsl8297: Want to systematically learn AI Engineering without jumping between papers, tutorials, and repos? Check out rohitg00/ai-engineering-from-scratch. This course has 10k+ stars, 435 lessons, 20 stages, covering from math foundations, neural networks...

name: agent-loop
description: ReAct-style loop for any tool list
phase: 14
lesson: 01

Contents

Phase 0: Setup & Tooling — `12 lessons`

Phase 1 — Math Foundations — 22 lessons

Phase 2 — ML Fundamentals — 18 lessons

Phase 3 — Deep Learning Core — 13 lessons

Phase 4 — Computer Vision — 28 lessons

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@Xudong07452910: Open-source high-quality tutorial recommendation: ai-engineering-from-scratch — A full-stack course from zero to production-grade AI engineering. This is a systematic AI engineering course built from scratch (Learn it. Build it. Ship it for others.…)

rohitg00/ai-engineering-from-scratch

@QingQ77: A full-stack AI engineering learning path from zero to mastery, 503 lessons covering from math basics to autonomous agent clusters, fully translated into Chinese with a dedicated website.

Submit Feedback

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@Russell3402: A friend wanted to learn AI engineering, but I couldn't come up with a good learning path for a while. Here I recommend an open-source AI engineering learning curriculum! It aims to take you from the ground up, covering the complete AI engineering stack: from math, machine learning, deep learning, Transformers, LLMs, Agents, MCP, multi-agent…

@Xudong07452910: Open-source high-quality tutorial recommendation: ai-engineering-from-scratch — A full-stack course from zero to production-grade AI engineering. This is a systematic AI engineering course built from scratch (Learn it. Build it. Ship it for others.…)

rohitg00/ai-engineering-from-scratch

@IndieDevHailey: This might be the hardest-core open-source AI engineering course on the internet. The GitHub trending project: ai-engineering-from-scratch has already gained 17.4k+ Stars. It's not another AI tutorial that teaches you how to call APIs; instead, it truly guides you to build AI systems from scratch.

@QingQ77: A full-stack AI engineering learning path from zero to mastery, 503 lessons covering from math basics to autonomous agent clusters, fully translated into Chinese with a dedicated website.

name: agent-loop description: ReAct-style loop for any tool list phase: 14 lesson: 01

Contents

Phase 0: Setup & Tooling — 12 lessons

Phase 1 — Math Foundations — 22 lessons

Phase 2 — ML Fundamentals — 18 lessons

Phase 3 — Deep Learning Core — 13 lessons

Phase 4 — Computer Vision — 28 lessons

Similar Articles

@Xudong07452910: Open-source high-quality tutorial recommendation: ai-engineering-from-scratch — A full-stack course from zero to production-grade AI engineering. This is a systematic AI engineering course built from scratch (Learn it. Build it. Ship it for others.…)

rohitg00/ai-engineering-from-scratch

@QingQ77: A full-stack AI engineering learning path from zero to mastery, 503 lessons covering from math basics to autonomous agent clusters, fully translated into Chinese with a dedicated website.

Submit Feedback

name: agent-loop
description: ReAct-style loop for any tool list
phase: 14
lesson: 01

Phase 0: Setup & Tooling — `12 lessons`