How to become the go-to AI Engineer—build a strong profile that generates opportunities.

Modules [130+ Hours]

  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 Value Module

Prerequisites

100 days AI challenge

7 Days Getting Started Challenge

Curriculum

<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

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

1.7 - Interview & Resources

Technical Interview Practice Questions

[Task] - Research Papers

</aside>