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What will you learn

  • Basic Python (enough to build things)

  • Git & Command Line usage

  • Python environments and libraries

  • Frontend (FE), Backend (BE), and Database (DB) integration

  • How to build your first web app that connects to LLMs

  • Generative AI: Prompting, Flexibility, and Controlling Responses

  • AI Agents: What they are, and how to build simple ones

  • Docker and how to run your project anywhere, and Deployments

Who is This For?

  • No coding experience? Coming from a non-tech background? This is for you. “Focus on fundamentals. Includes examples that will get harder.” Then, slowly add in each component. E.g. FE, BE, DB.

  • All you need: a MacBook and willingness to learn

Introduction to AI Engineering (PART1) - course introduction

What is the course?

Introduction to AI Engineering 00 - Setup

What does it include?

Setting up various accounts and software such as google colab,

render.com, anaconda, python, docker, vs code, git, github.

Introduction to AI Engineering 01 - Introduction to Python, Cli, GitHub

What does it include?

Comprehensive foundation course covering essential development tools and Python programming. Students learn command-line interface navigation, Git version control, GitHub repository management, and Python syntax fundamentals. This extensive session establishes the core technical skills needed for AI engineering projects, including code versioning, and basic programming concepts.

Introduction to AI Engineering 012 - หากซวยเจอ Data Structure & Algorithm interview จริงๆ นี้จะเป็น basic ที่ควรรู้

What does it include?

Why learn Data Structures and Algorithms?

An introductory overview of Data Structures and Algorithms, highlighting their importance for software engineering interviews and writing efficient code covering common data structures and key algorithms explaining their purpose and complexity.

Introduction to AI Engineering 021- creating virtual environments

What does it include?

Focused tutorial on Python virtual environment management using tools like venv and conda. Covers best practices for isolating project dependencies, avoiding package conflicts, and maintaining clean development environments. Essential for managing different AI/ML library versions across multiple projects.

Introduction to AI Engineering 022 - applications of python

What does it include?

Deep dive into Python's practical applications in AI and data science. Explores popular libraries like NumPy, Pandas, beautiful soup, generative AI libraries. Demonstrates news scraping use case and summarization with generative AI.

Additionally, includes foundation concept of using Large Language models and prompting.

Introduction to AI Engineering 031 - Basics of Web applications

What does it include?

Introduction to web development concepts essential for AI applications. Covers everything in python. Client-server architecture, and how web technologies integrate with AI systems. Provides foundation for building user interfaces that interact with AI models and services. 

Tech stack: Fastapi, streamlit, gemini APIs

Introduction to AI Engineering 032 - Deploy your frontend service

What does it include?

Practical guide to deploying web applications and frontend services. Includes hands-on deployment exercises using free platform like render.com

Introduction to AI Engineering 033 - RESTAPI and CRUD

What does it include?

Essential web development concepts focusing on RESTful API design and CRUD (Create, Read, Update, Delete) operations. Demonstrates how to build APIs that serve Restaurant booking use case, handle data persistence with SQLite database

Introduction to AI Engineering 041 - Structured Outputs ให้ LLM ตอบตรงตาม format ที่เราต้องการ

What does it include?

Advanced prompting techniques for controlling Large Language Model outputs. Teaches how to constrain AI responses to specific formats like JSON or custom schemas. Critical for building reliable AI applications that require predictable, parseable responses for downstream processing.

Introduction to AI Engineering 042 - Few Shot Prompting

What does it include?

Concise introduction to few-shot learning techniques for LLMs. Demonstrates how to provide examples within prompts to guide model behavior and improve output quality. Covers prompt engineering strategies for achieving better results with minimal training data.

Introduction to AI Engineering 043 - พื้นฐานสำคัญของ RAG ก่อนสร้าง AI Agent ใน 25 นาที!

What does it include?

Comprehensive introduction to Retrieval-Augmented Generation (RAG) systems. Explains how RAG combines external knowledge retrieval with language generation to create more accurate, up-to-date AI responses. Foundation knowledge essential before building AI agents.

Introduction to AI Engineering 044 - โค้ด RAG ใน15นาที! (สั่งการบ้านแล้วทำด้วย) #ก่อนลุย-AI-Agents

What does it include?

Fast-paced, hands-on coding session building a complete RAG system. Practical implementation tutorial showing real code examples and demonstrating the homework assignment process. Bridges theory from the previous session with actual working code.

Introduction to AI Engineering 045 - Building end to end RAG with Milvus แยก Frontend, Backend ด้วย

What does it include?

Simple Naive RAG architecture tutorial using Milvus vector database. Demonstrates proper separation of concerns with distinct frontend and backend services. Covers scalable RAG system design, vector storage, similarity search.

Introduction to AI Engineering 045.2 - อะไรคือ Reranker? ควรรู้ในการพัฒนา advance RAG

What does it include?

Explanation of what the differences between bi-encoder & cross-encoder and how reranker can help improve retrieval accuracy. Walk through on how cross-encoder works.

Introduction to AI Engineering 046 - AI Agents ฉบับ อาม่าก็เข้าใจ

What does it include?

Beginner-friendly introduction to AI agent concepts and architecture. Breaks down complex agent systems into understandable components, explaining how agents perceive, reason, and act in their environments. Provides conceptual foundation for agent development.

Introduction to AI Engineering 046.2 (Bonus) - vanilla RAG และ Agentic Rag ต่างกันยังไง?

What does it include?

Many people wonder what are the differences between how Vanilla RAG & Agentic RAG works. This short clip will give you an example.

Introduction to AI Engineering 047 - Coding a Single AI Agents with me with gemini and Langchain

What does it include?

Hands-on coding session building a single AI agent using Google's Gemini model and LangChain framework. Demonstrates practical agent implementation, tool integration, and decision-making workflows. Students follow along to create their own functional AI agent.

Introduction to AI Engineering 048 - Human in the loop routing with langgraph

What does it include?

Sometimes, AI Agent alone makes the system autonomous. Here I walk through a shopping example, on how we can use routing with human in the loop to make system more robust and predictable while still having some "intelligence"

Introduction to AI Engineering 049 - What is a multi agent system?

What does it include?

Focus on a type of multi-agent: Handoffs

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Introduction to complex multi-agent architectures where multiple AI agents collaborate, compete, or coordinate to solve problems. Covers agent communication protocols, system design patterns, and use cases where multiple agents outperform single-agent solutions.

Introduction to AI Engineering 0410 - Coding Multi-agent Systems with me

What does it include?

Focus on a type of multi-agent: Handoffs.

​

Introduction to complex multi-agent architectures where multiple AI agents collaborate, compete, or coordinate to solve problems. Covers agent communication protocols, system design patterns, and use cases where multiple agents outperform single-agent solutions.

Introduction to AI Engineering 0411 - multiagents มีแบบไหนบ้าง

What does it include?

Beyond handoff multi-agent, what agents are there?

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Types of multi-agent e.g. concurrent, sequential etc.

Introduction to AI Engineering 0412 - Multi-modal LLM พร้อม 7 use cases

What does it include?

When tasks extend traditional text input and output, multimodality LLM can help you. Let's have a look at 7 different use cases and try code it out

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Pick A problem to Solve (BONUS)

Here I pick a popular problem and provide recommendations on how to solve it as a begineer

EP1 - หยิบปัญหามาแก้ สรุปเอกสาร finance

Problem Statement:

​

An analyst needs to read a lot of financial documents

and extract key points from various source then summarize the information. Here, I walk to how to solve the problem with minimum effort and how you should get started

EP2 - Designing Employee Support HR Agent

Problem Statement:

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Build an HR chatbot that handles core employee queries. The system must support five key areas: organizational information, compensation details, leave management, employee data, and policy guidance.

Extra Free videos to get you further

This course does not include everything in the AI & Software world, so here are few videos to help you go beyond.

What will you get?

  • 6–7 hr videos

  • Join the Discord community — post your questions, and I’ll help when I can

What it will not include

  • - No code tool teaching

  • - Not guaranteeing you a job.

  • - Only part 1 (not including BQ, Spark, CICD, monitor, traditional AI/ML) → this will be covered in the 2nd half.

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