The Roadmap

AI Engineering Curriculum

A structured 5-phase journey from Python basics to Agentic AI

01Foundations
02Data Science
03AI Core
04Generative AI
05Agentic AI
01

Foundations

Python programming and AI fundamentals

Module 01

Introduction to Artificial Intelligence

AI Fundamentals

  • What is Artificial Intelligence
  • History and Evolution of AI
  • Types of AI: Narrow, General, Super

AI Applications

  • AI Applications in Industry

AI Ethics

  • Ethics and Responsible AI

AI Future

  • Future Trends in AI
Module 02

Python & Object-Oriented Programming

Python Basics

  • Environment Setup
  • Variables and Data Types
  • Strings, Lists, Tuples, Sets, Dictionaries
  • Control Flow and Loops
  • Functions and Lambda
  • File Handling

OOP Concepts

  • Object-Oriented Programming
  • Classes and Objects
  • Inheritance and Polymorphism
  • Encapsulation

Advanced Python

  • Decorators and Generators
02

Data Science

Data manipulation, visualization, and preprocessing

Module 03

Exploratory Data Analysis with NumPy & Pandas

NumPy

  • Arrays and Operations
  • Slicing and Reshaping
  • Broadcasting

Pandas

  • Series and DataFrames
  • Data Loading and Exporting
  • Missing Data Handling
  • Grouping and Aggregation
  • Merging and Joining
Module 04

Data Visualization

Visualization Libraries

  • Matplotlib Basics
  • Seaborn for Statistical Plots
  • Plotly Interactive Charts

Dashboard Design

  • Dashboard Design Principles
Module 05

Data Preprocessing & ETL

Data Preparation

  • Feature Engineering
  • Feature Selection
  • Cross Validation

Data Pipelines

  • Building Data Pipelines
  • Scaling and Normalization
03

AI Core

Machine Learning, Deep Learning, and Computer Vision

Module 06

Machine Learning

Supervised Learning

  • Regression Algorithms
  • Classification Algorithms
  • Model Evaluation Metrics

Unsupervised Learning

  • Clustering Techniques
  • Dimensionality Reduction

Advanced ML

  • Hyperparameter Tuning
  • Ensemble Methods
  • Cross Validation Strategies
Module 07

Deep Learning

Neural Networks

  • ANN Architecture
  • Activation Functions
  • Backpropagation
  • Optimization Algorithms

Frameworks

  • TensorFlow
  • PyTorch
Module 08

Computer Vision

CV Techniques

  • CNN Architectures
  • Image Classification
  • Object Detection
  • Transfer Learning
  • Image Segmentation
04

Generative AI

NLP and Large Language Models

Module 09

Natural Language Processing

Text Processing

  • Tokenization
  • Word Embeddings (Word2Vec, GloVe)

Sequence Models

  • RNN, LSTM, GRU
  • Transformers Architecture

Modern NLP

  • Hugging Face Tools
  • Text Classification
  • Text Generation
Module 10

Large Language Models

LLM Engineering

  • Prompt Engineering
  • LLM APIs Integration
  • Fine-Tuning Basics

Advanced LLM

  • RAG Systems
  • AI Agents Design
  • LangChain Framework
  • LangGraph Workflows
05

Agentic AI

Autonomous AI systems and workflow automation

Module 11

AI Agents & RAG Systems

RAG Architecture

  • Vector Databases
  • Document Chunking
  • Embedding Models
  • Retrieval Strategies

Agent Design

  • Agent Architecture
  • Tool Use and Function Calling
  • Memory Systems
  • Multi-Agent Orchestration
Module 12

Capstone & Deployment

Production AI

  • FastAPI for AI Services
  • Model Deployment
  • Cloud Integration

Final Project

  • End-to-End AI System
  • Portfolio Building
  • Industry Best Practices

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