AI Engineering from Scratch Prerequisites: Complete Setup and Curriculum Guide

To begin the AI Engineering from Scratch curriculum, you need Git, Python 3.11 or newer, and the ability to write code in any language (Python is most convenient), with each subsequent phase requiring completion of specific previous phases and lessons.

The AI Engineering from Scratch curriculum by Rohit Gupta (rohitg00/ai-engineering-from-scratch) is structured as a progressive, phase-based learning path that requires strict adherence to prerequisite chains. Unlike traditional courses that allow random access, this repository enforces a build-upon-previous-knowledge model where each phase's README.md explicitly defines the required foundation before you can attempt its lessons.

General Prerequisites for AI Engineering from Scratch

Before touching any machine learning code, the repository defines two categories of baseline requirements in the root README.md and phases/00-setup-and-tooling/README.md.

Core Technical Skills

• Programming Ability: You must be able to write code in any language. Python is the most convenient choice because the entire curriculum uses Python-based labs and examples.
• Git Installation: Version control is mandatory for cloning the repository and managing your progress through the phases.
• Python 3.11+: The phases/00-setup-and-tooling/README.md explicitly requires Python 3.11 or newer to ensure compatibility with modern AI/ML libraries used throughout the curriculum.

Learning Mindset

The root README.md emphasizes a desire to understand how AI works internally, not just how to call APIs. This curriculum focuses on building systems from first principles rather than using high-level abstractions.

Phase-by-Phase Prerequisites

Each phase directory contains a README.md that acts as a gatekeeper. You cannot skip phases because later lessons depend on specific knowledge from earlier ones.

Phase 0 – Setup and Tooling

Required: Git and Python 3.11+ installed on your system.

phases/00-setup-and-tooling/README.md specifies that this phase requires no prior knowledge. It exists solely to verify your development environment. The repository provides a verification script to confirm your setup:

# Run the pre-flight verifier from the repository root

python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner

If any prerequisite is missing, the script outputs a "Next:" command indicating exactly which lesson to complete.

Phase 1 – Math Foundations

Required: Completion of Phase 0 (or confirmation that Git and Python work correctly).

phases/01-math-foundations/README.md builds upon the tooling setup, introducing linear algebra and calculus concepts necessary for all subsequent AI work.

Phase 2 – ML Fundamentals

Required:

• Phase 1 Math Foundations
• Familiarity with NumPy

The phases/02-ml-fundamentals/README.md explicitly lists NumPy familiarity as a non-negotiable prerequisite, as vectorized operations form the basis of the hands-on implementations.

Phase 3 – Deep Learning Core

Required: Phase 1 Linear Algebra intuition (mandatory) and Phase 2 (recommended).

According to phases/03-deep-learning-core/README.md, you need the mathematical foundations from Phase 1 to understand backpropagation and gradient descent implemented in this phase.

Phase 4 – Computer Vision and Phase 6 – Speech & Audio

Required for both: Phase 1 vectors, matrices, and probability.

phases/04-computer-vision/README.md additionally requires Phase 3 Lesson 11 specifically. Phase 6 (phases/06-speech-and-audio/README.md) shares the same Phase 1 mathematical prerequisites but focuses on temporal signal processing rather than spatial features.

Phase 5 – NLP Foundations to Advanced

Required: Phase 2 Lesson 14 (Naïve Bayes).

phases/05-nlp-foundations-to-advanced/README.md specifies this exact lesson because probabilistic classification concepts are reused immediately in text processing algorithms.

Phase 7 – Transformers Deep Dive

Required: Phase 3 Deep Learning Core and Phase 5 Lesson 09 (attention mechanisms).

The phases/07-transformers-deep-dive/README.md explicitly gates this content behind understanding both deep learning fundamentals and attention mechanisms from the NLP phase.

Phase 8 – Generative AI

Required: Phase 2 ML Fundamentals, Phase 3 Deep Learning Core, and Phase 7 Transformers.

phases/08-generative-ai/README.md requires all three foundational pillars as generative models combine statistical learning, neural architectures, and transformer backbones.

Phase 9 – Reinforcement Learning

Required: Phase 1 probability and distributions, plus specific Phase 2 lessons.

phases/09-reinforcement-learning/README.md leverages the probability theory from Phase 1 for policy gradients and Markov decision processes.

Phase 10 – LLMs From Scratch

Required: Phase 5 NLP Foundations and Phase 7 Transformers (strongly recommended).

phases/10-llms-from-scratch/README.md bridges natural language processing with transformer architectures to build language models from the ground up.

Phase 11 – LLM Engineering

Required: Phase 10 lessons 01-05.

phases/11-llm-engineering/README.md requires specific completion of the first five lessons from the LLM construction phase, covering architecture implementation and training basics.

Phase 12 – Multimodal AI

Required: Phase 7 Transformers and Phase 4 Computer Vision.

phases/12-multimodal-ai/README.md combines sequence modeling (Transformers) with visual feature extraction (Computer Vision) to process heterogeneous data types.

Phase 13 – Tools & Protocols

Required: Phase 11 LLM completion APIs.

phases/13-tools-and-protocols/README.md assumes you can interact with LLM APIs (covered in Phase 11) before introducing Model Context Protocol (MCP) and Agent Skills.

Phase 14 – Agent Engineering

Required: Phase 11 LLM Engineering and Phase 13 Tools & Protocols.

phases/14-agent-engineering/README.md synthesizes API usage with tool-calling frameworks to build autonomous agent loops.

Phase 15 – Autonomous Systems

Required: Phase 14 Lesson 01 (The Agent Loop).

phases/15-autonomous-systems/README.md specifically requires understanding the fundamental agent execution loop before scaling to complex autonomous behaviors.

Phase 16 – Multi-Agent and Swarms

Required: Phase 14 Agent Engineering, plus Node.js 20+ and npx.

Unique among the phases, phases/16-multi-agent-and-swarms/README.md introduces JavaScript tooling requirements for distributed agent orchestration.

Phase 17 – Infrastructure and Production

Required: Phase 11 LLM Engineering and Phase 13 Tools & Protocols.

phases/17-infrastructure-and-production/README.md focuses on deployment patterns requiring both model serving knowledge (Phase 11) and protocol integration (Phase 13).

Phase 18 – Ethics, Safety and Alignment

Required: Phase 10 Lessons 06-08 on SFT (Supervised Fine-Tuning), RLHF, and DPO.

phases/18-ethics-safety-alignment/README.md requires understanding fine-tuning and alignment techniques before discussing their ethical implications and safety guardrails.

Phase 19 – Capstone Projects

Required: Completion of the listed preceding phases for the chosen project.

phases/19-capstone-projects/README.md acts as the final synthesis, requiring you to have completed the specific prerequisite chain relevant to your selected capstone track.

Verifying Your Prerequisites Programmatically

The repository includes automated verification tools to prevent you from starting a phase unprepared. The verification script checks for required tools and previous lesson completions:

# Verify readiness for the beginner route

python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner

The script exits with code 0 only when all prerequisites are satisfied. If validation fails, it outputs the exact lesson you must complete next, eliminating guesswork from the curriculum path.

Summary

• Minimum entry requirement: Git and Python 3.11+ (Phase 0 only).
• Mathematical foundation: Phase 1 (linear algebra, calculus) is required for all deep learning and AI phases.
• Library dependency: NumPy familiarity is mandatory starting Phase 2.
• Strict progression: Phases 7, 11, 14, and others explicitly require specific lesson numbers from previous phases, not just general phase completion.
• Tooling expansion: Phase 16 introduces Node.js 20+ and npx requirements for multi-agent systems.
• Verification: Use phases/00-setup-and-tooling/01-dev-environment/code/verify.py to check readiness before starting any phase.

Frequently Asked Questions

What programming language do I need for AI Engineering from Scratch?

You need the ability to write code in any language, though Python is strongly preferred and used exclusively throughout the curriculum. The phases/00-setup-and-tooling/README.md assumes Python 3.11+ for all labs and verification scripts, while phases/16-multi-agent-and-swarms/README.md additionally requires Node.js 20+ for specific multi-agent exercises.

Can I skip Phase 0 if I already know Git and Python?

No. Even if you have Git and Python 3.11+ installed, you must verify your environment using the verification script in phases/00-setup-and-tooling/01-dev-environment/code/verify.py. Additionally, phases/01-math-foundations/README.md explicitly lists "Completion of Phase 0" as its prerequisite, making Phase 0 a formal gate in the prerequisite chain regardless of prior experience.

What is the minimum version of Python required?

Python 3.11 or newer is mandatory. This requirement is specified in phases/00-setup-and-tooling/README.md to ensure compatibility with type hinting features and modern async patterns used in later phases, particularly in Phase 11 (LLM Engineering) and Phase 13 (Tools & Protocols).

How do I verify I have completed the prerequisites for a specific phase?

Run the pre-flight verification script from the repository root with the appropriate route flag. The script at phases/00-setup-and-tooling/01-dev-environment/code/verify.py checks your tooling installation and lesson completion status, exiting successfully only when you satisfy all dependencies for your target phase. If prerequisites are missing, the script provides the exact command to complete the next required lesson.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}