Become an AI Engineer who Built, Deploy & Market Production Grade LLM Apps Without Years of Struggle or a PhD!

Prerequisites

7 Steps AI Bootcamp Prep Challenge

Curriculum

1. Foundations of AI Engineering
2. Mastering Large Language Models (LLMs)
3. Retrieval-Augmented Generation (RAG)
4. Fine-Tuning LLMs
5. Reinforcement Learning and Ethical AI
6. Agentic Workflows
7. Career Acceleration
8. Bonus

100 Days AI Engineering Challenge

<aside> [Module - 1]

Foundations of AI Engineering

Duration: 20 Hours

1.1 - Python

1.1.1 - [Hands-On] Functions & Higher Order Functions

1.1.2 - [Hands-On] Modules, Packages, Library & Framework

1.1.3 - [Hands-On] OOPs [Object Oriented Programming]

1.1.4 - [Hands-On] Data Structures & Algorithms

1.1.5 - [Hands-On] Data Manipulation [NumPy & Pandas]

1.2 - Mathematics in AI

1.2.1 - Linear Algebra

1.2.2 - Calculus

1.2.3 - Statistics & Probability

1.3 - Overview of the AI Ecosystem

1.3.1 - AI and its Evolution

1.3.2 - AI vs ML vs DL vs GenAI vs LLM vs ChatGPT

1.3.3 - LLM Ecosystem - ChatGPT, Grok, HuggingFace

1.3.4 - AI Market Analysis & Career Opportunity

1.3.5 - AI Use Cases & Tools

1.4 - Machine Learning as of 2025

1.4.1 - All you need to know about Machine Learning

1.4.2 - [Hands-On] Building a Classification Model

1.4.3 - [Hands-On] Building Multiple Linear Regression model

1.4.4 - When to use Which ML Algorithm?

1.5 - Deep Learning as of 2025

1.5.1 - [Hands-On] Building Your First Neural Network

1.5.2 - [Hands-On] Activation Functions from Scratch

1.5.3 - Drawbacks in RNN, CNN, LSTM architecture

1.6 - The Project Lab [Build-Deploy-Market]

[The Project Lab - 01] AI-powered Resume Analyzer using Python, Flask & NLP

[Thus Showcase] - Show your project publicly [Community/YouTube/GitHub]

1.7 - Interview & Resources

Technical Interview Practice Questions

[Task] - Research Papers

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<aside> [Module - 2]

Mastering Large Language Models (LLMs)

Duration: 20 Hours

2.1 - LLM Ecosystem and Access

2.1.1 - Introduction to Transformer Architecture

2.1.2 - LLM Model Architectures

2.1.3 - How to train LLMs?

2.1.4 - [Cloud providers] Azure Open AI, AWS Bedrock, GCP Vertex AI

2.1.5 - [Open-source LLMs] DeepSeek, LLaMA, Mistral 7b (via Hugging Face)

2.1.6 - [Hands-On] Setup LLM on your Local machine using Ollama

2.1.7 - [Hands-On] Sentiment classification pipeline for Amazon product reviews

2.2 - Enterprise Applications

2.2.1 - Business problems solved by LLMs

2.2.2 - Workflow for developing LLM-based applications

2.2.3 - [Hands-On] Azure Open AI’s Python API to generate text

2.2.4 - [Cost-benefit analysis] Cloud vs. on-premise

2.2.5 - [Hands-On] HR query bot and outline of workflow

2.2.6 - Multimodal AI Systems

2.2.6 - Vision Models

2.3 - Prompt Engineering

2.3.0 - What is prompt engineering?

2.3.1 - Zero-shot & Few-shot

2.3.2 - Chain-of-Thought & Tree-of-Thought

2.3.3 - Designing prompts for evaluation [LLM as a judge]

2.3.4 - [Hands-On] Design zero-shot and few-shot prompts using Azure AI

2.3.5 - [Hands-On] CoT prompt to solve a math problem

2.4 - System Design

2.4.1 - The 7 Step ML System Design Framework

2.4.2 - Pinterest - Visual Search ML System

2.4.3 - How to build a GenerativeAI Platform?

2.5 - The Project Lab [Build-Deploy-Market]

[The Project Lab - 02] Building LLM from Scratch

[Thus Showcase] - Show your project publicly [Community/YouTube/GitHub]

2.6 - Interview & Resources

Technical Interview Practice Questions

Resources

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