Zero to AI Engineer: Full-Stack AI Engineering
A complete 20-week AI engineering journey covering Python, data science, mathematics, machine learning, deep learning, computer vision, NLP, transformers, LLMs, RAG, FastAPI, LangChain, LangGraph and production AI deployment.
Go from beginner programmer to full-stack AI engineer by building real AI systems step by step.
This is a complete 20-week AI engineering roadmap designed for learners who want to build real AI applications, not just watch theory. You will begin with Python programming foundations, move into data science and mathematics, then build machine learning and deep learning models, explore transformers and LLMs, design RAG pipelines, and finally deploy production-grade agentic AI systems with FastAPI, LangChain, LangGraph, Docker, CI/CD and observability. By the end, you will have a complete portfolio of AI projects proving that you can build, train, integrate and deploy AI systems end to end.
Most AI courses teach disconnected topics. This course turns you into a full-stack AI engineer.
You will learn the complete AI engineering stack from foundation to production:
Write production-quality Python using functions, OOP, async and concurrency
Analyze and visualize real-world data using NumPy, Pandas, Matplotlib and Seaborn
Understand the mathematics behind machine learning, optimization and statistics
Train, evaluate and tune machine learning models using scikit-learn
Build deep learning models using TensorFlow and PyTorch
Train CNNs for computer vision and RNN/LSTM models for sequence tasks
Understand transformers, attention mechanisms and pretrained language models
Build LLM applications with prompt engineering and structured outputs
Design RAG systems with document processing, vector databases and retrieval pipelines
Build production AI APIs with FastAPI, LangChain, LangGraph and Docker
Deploy, monitor and scale AI systems with MLOps, CI/CD and observability practices
Master the modern stack
20 modules · 454 lectures
1.1 Introduction to Programming & Python
- 1What programming is and why Python is the best starting point for AI
- 2Python installation, setup and virtual environments
- 3Python interpreter, REPL and script execution
1.2 Core Data Types & Operators
- 1Variables, assignment and naming conventions
- 2Numeric types, arithmetic and comparison operators
- 3Strings, indexing, slicing and formatting
1.3 Collections & Data Structures
- 1Lists, mutation and common methods
- 2Tuples and immutability use cases
- 3Dictionaries for key-value storage and lookup
- 4Sets for uniqueness, intersection and union operations
Calculator and String Processor
Build small Python programs that perform calculations, process strings and use lists, dictionaries and sets to organize data.
Week 1 assignments
20 hands-on builds. One serious portfolio.
5 phases. 5 production-ready systems.
Python Engineering Toolkit
Phase 1 — Combines Modules 1, 2, 3 and 4
Data Analysis & ML Math Portfolio
Phase 2 — Combines Modules 5 and 6
Machine Learning & Deep Learning Portfolio Suite
Phase 3 — Combines Modules 7–12
Generative AI Knowledge Assistant
Phase 4 — Combines Modules 13–16
Production Agentic AI System
Phase 5 — Final Capstone
You will ship 3 guided capstones — chosen from high-impact tracks
You will graduate with 3 production-grade capstone systems — guided step-by-step by your instructor. The full briefs, blueprints, and starter assets are unlocked the moment you enroll.
Capstone Project Track A
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Capstone Project Track B
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Capstone Project Track B
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- Beginners who want a structured path into AI engineering
- Students who want a complete AI portfolio for jobs, internships or graduate applications
- Software developers who want to move into machine learning, generative AI and agentic systems
- Data enthusiasts who want to learn Python, ML, deep learning and deployment together
- Backend developers who want to build AI-powered APIs and production AI systems
- Freelancers who want to build AI applications for clients
- Startup founders who want to understand and prototype AI products end to end
- Anyone who wants to go from zero to building deployable AI systems
- No prior AI or machine learning experience required
- Basic computer literacy
- High-school level mathematics is helpful
- A laptop capable of running Python, notebooks and basic Docker workflows
- Consistency and willingness to practice 6–7 hours per week
Start from zero. Graduate as an AI engineer with real projects.
By the end of Zero to AI Engineer, you will not just understand AI concepts. You will have written Python programs, analyzed data, trained machine learning and deep learning models, built RAG and GenAI applications, created agentic workflows and deployed a production-style AI system.
You will finish with
- A complete 20-week AI engineering roadmap
- 20+ hands-on assignments
- 5 phase projects
- 1 final guided capstone
- A portfolio covering Python, ML, DL, GenAI, RAG and Agentic Systems
Ready to build it for real?
Join Inception 23 Academy and ship production-grade systems in weeks, not months.