AI Builder: From Zero to Deployed AI App

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About Course

👋 A welcome from your AI strategist

You learn to build AI by building it. In this course you go from a raw dataset to a working, evaluated AI solution, and you learn the engineering judgement that separates a demo from something people can rely on.

Work through it at your own pace, try every practice task before opening the model answer, and look for my 🏛 Architect’s Notes along the way.

Mahesh Raja Vandyala
AI Strategist and Enterprise Architect · LinkedIn

🐍 Never written Python? Start with Module 0.

Module 0 walks you through installing Python and VS Code, virtual environments, installing libraries and running your first program on Windows or Mac. Every lesson after it tells you exactly which file to create, what to type in the terminal, and the output you should see, with a “what to do if something goes wrong” table.

Go from basic coding to building and deploying a real AI application, and graduate with a portfolio that gets you noticed. Follow Alex Carter, a graduate intern at a fictional logistics company, as he builds “Nimbus Assist” module by module, and build your own version alongside him.

Who it’s for

Fresh graduates and final-year students with basic programming in any language. Take the 3 common courses first.

What makes this course different

One real project built across every module: data analysis → ML → deep learning → LLM extraction → RAG → agents → deployment
Real datasets and documents to download: shipment data, a company policy, labelled support emails, plus eval and README templates
Full code, and verified results: every number in the ML lessons was produced by actually running the code
Labs with model solutions, scenario quizzes and Architect’s Notes on how this works in real companies
A portfolio-grade capstone with a professional rubric

What you’ll learn

Python, pandas, data cleaning and exploratory analysis
Framing, training and evaluating ML models with scikit-learn, and avoiding leakage
Neural networks and transfer learning with PyTorch
LLM APIs with validated structured outputs
RAG with embeddings, vector search, citations and refusals
Tool-using agents, MCP, and agent safety
Eval suites, AI security (OWASP LLM Top 10), deployment, monitoring and model cards

Format

7 modules + capstone · 35 lessons · 7 scenario quizzes · about 10 weeks at 8–10 hours a week. 70% hands-on.

Last reviewed: November 2026. Libraries and models change quickly, so this course is reviewed quarterly.

Course Content

Module 0 — Start Here: Python Setup for Complete Beginners
Never written Python? Start here. Install Python and VS Code, create a virtual environment, install libraries, and run your first program, with exact commands and expected output.

0.1 Install Python and Check It Works
0.2 VS Code, Your Project Folder and the Terminal
0.3 Virtual Environments: One Toolbox per Project
0.4 Installing Libraries: pandas, NumPy, matplotlib and scikit-learn
0.5 Your First Python Program: Create, Run, Check
0.6 When Things Go Wrong: Reading and Fixing Errors
Module 0 Quiz: Python Setup

Module 1 — Python & Data for AI
Python essentials, pandas, data cleaning and charts, every step with the exact file, command and expected output.

Module 2 — Machine Learning Essentials
Frame an ML problem, train and compare models, evaluate honestly, avoid leakage, every script with the exact expected output.

Module 3 — Deep Learning & Neural Networks
Neural networks and transfer learning in Google Colab: every notebook cell, what it does and what output to expect.

Module 4 — Building with LLM APIs
Get an API key, store it safely, make your first call, get validated JSON, stream conversations and estimate cost, all step by step.

Module 5 — LLM Apps & Retrieval-Augmented Generation (RAG)
Embeddings, a complete RAG pipeline in one file, debugging retrieval, a Streamlit app and an evaluation script, step by step.

Module 6 — AI Agents & Tool Use
Build a tool-using agent step by step, with exact tool outputs, a step limit, trace logging and safety tests.

Module 7 — Evaluate, Secure & Deploy
Run your evals before every change, secure the app, and deploy it to a public URL step by step.

Capstone — Ship Your Own AI App
Plan, build, evaluate, secure and deploy an AI application that solves a real problem, then present it like a professional.

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AI Builder: From Zero to Deployed AI App

About Course

Who it’s for

What makes this course different

What you’ll learn

Format

Course Content

Module 0 — Start Here: Python Setup for Complete Beginners Never written Python? Start here. Install Python and VS Code, create a virtual environment, install libraries, and run your first program, with exact commands and expected output.

0.1 Install Python and Check It Works

0.2 VS Code, Your Project Folder and the Terminal

0.3 Virtual Environments: One Toolbox per Project

0.4 Installing Libraries: pandas, NumPy, matplotlib and scikit-learn

0.5 Your First Python Program: Create, Run, Check

0.6 When Things Go Wrong: Reading and Fixing Errors

Module 0 Quiz: Python Setup

Module 1 — Python & Data for AI Python essentials, pandas, data cleaning and charts, every step with the exact file, command and expected output.

1.0 The Project, the Dataset and Your Project Folder

1.1 Python Essentials for AI Work

1.2 pandas: Your Data Workbench

1.3 Cleaning Data: Where Real Projects Start

1.4 Exploring and Visualising Data

Lab & Mini-Project: Why Are Deliveries Late?

Module 1 Quiz: Python & Data

Module 2 — Machine Learning Essentials Frame an ML problem, train and compare models, evaluate honestly, avoid leakage, every script with the exact expected output.

2.1 Framing an ML Problem

2.2 Training and Comparing Models with scikit-learn

2.3 Evaluation: Why Accuracy Can Lie

2.4 Features, Leakage and Explainability

Lab & Mini-Project: Predict Late Deliveries

Module 2 Quiz: Machine Learning Essentials

Module 3 — Deep Learning & Neural Networks Neural networks and transfer learning in Google Colab: every notebook cell, what it does and what output to expect.

3.1 From Neuron to Network (and Your First Colab Notebook)

3.2 How Networks Learn: Loss, Gradients and Training Loops

3.3 Architectures: CNNs, Transformers and Beyond

3.4 Transfer Learning: Big Results from Small Data

Lab & Mini-Project: Damaged Parcel Classifier

Module 3 Quiz: Deep Learning

Module 4 — Building with LLM APIs Get an API key, store it safely, make your first call, get validated JSON, stream conversations and estimate cost, all step by step.

4.1 Your First LLM API Call (Key, .env and Code)

4.2 Structured Outputs: Reliable JSON Every Time

4.3 Conversations, Streaming, Cost and Model Choice

Lab & Mini-Project: Support Email Triage

Module 4 Quiz: Building with LLM APIs

Module 5 — LLM Apps & Retrieval-Augmented Generation (RAG) Embeddings, a complete RAG pipeline in one file, debugging retrieval, a Streamlit app and an evaluation script, step by step.

5.1 Embeddings and Vector Search

5.2 The RAG Pipeline End to End

5.3 Making RAG Good: Chunking, Retrieval and Refusals

5.4 Shipping a UI with Streamlit

Lab & Mini-Project: Policy Assistant with Citations

Module 5 Quiz: RAG

Module 6 — AI Agents & Tool Use Build a tool-using agent step by step, with exact tool outputs, a step limit, trace logging and safety tests.

6.1 From Chatbot to Agent

6.2 Tool Calling in Practice: Build the Agent

6.3 Connecting to Real Systems with MCP

6.4 Agent Safety: Limits, Approvals and Prompt Injection

Lab & Mini-Project: Nimbus Assist Agent

Module 6 Quiz: Agents & Tool Use

Module 7 — Evaluate, Secure & Deploy Run your evals before every change, secure the app, and deploy it to a public URL step by step.

7.1 Evaluating LLM Applications

7.2 Securing AI Applications

7.3 Deploying and Monitoring

Lab & Mini-Project: Ship Nimbus Assist

Module 7 Quiz: Evaluate, Secure & Deploy

Capstone — Ship Your Own AI App Plan, build, evaluate, secure and deploy an AI application that solves a real problem, then present it like a professional.

Capstone Brief, Milestones and Rubric