This comprehensive AI Engineering with Generative AI and Agent Systems Course is an advanced, career-oriented online program designed for learners who want to build strong expertise in Artificial Intelligence, Machine Learning, Deep Learning, and modern Generative AI technologies.

The course follows a structured learning path starting from Python and AI fundamentals, progressing through machine learning, deep learning, computer vision, and ultimately reaching cutting-edge topics such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and autonomous AI agents.

It is ideal for college students, aspiring AI engineers, software developers, and professionals who want to transition into high-demand AI roles. The program emphasizes practical learning through real-world projects, hands-on coding, and deployment training to ensure learners gain job-ready skills.

PHASE 1: AI & Python Foundations (Beginner Level)

Module 1: Python Programming for AI

• Python Basics (Variables, Data Types)

• Conditions & Loops

• Functions

• OOPS (Encapsulation, Inheritance, Polymorphism)

• NumPy (Arrays & Math Ops)

• Pandas (Data Handling)

• Data Cleaning

• Data Visualization

• Basic Statistics for AI

PHASE 2: Machine Learning Foundations

Module 2: Introduction to Machine Learning

• What is AI vs ML vs DL

• Types of ML (Supervised/Unsupervised)

• Dataset Understanding

• Train-Test Split

• Linear Regression

• Logistic Regression

• KNN

• Naive Bayes

• Decision Trees

• Model Evaluation (Accuracy, Precision, Recall, F1)

• Overfitting & Underfitting

PHASE 3: Advanced ML Engineering (Intermediate)

Module 3: Ensemble & Advanced ML

• Random Forest

• Bagging vs Boosting

• AdaBoost

• Gradient Boosting

• XGBoost

• LightGBM

• CatBoost

• Feature Engineering

• Cross Validation

• Hyperparameter Tuning

• Handling Imbalanced Data (SMOTE)

• ML Pipelines

• Model Interpretability (SHAP Intro)

PHASE 4: Deep Learning

Module 4: Neural Networks

• Perceptron Concept

• Activation Functions

• Forward Propagation

• Backpropagation

• Loss Functions

• Optimizers

Module 5: CNN & Sequence Models

• CNN Basics

• Convolution & Pooling

• Image Classification

• RNN Basics

• LSTM & GRU

• Transfer Learning

PHASE 5: Computer Vision with OpenCV

Module 6: OpenCV & Vision Systems

• Image Representation

• Image Processing

• Edge Detection

• Contour Detection

• Face Detection

• Real-time Webcam App

• Object Detection Concept (YOLO Intro)

• CNN for Image Classification

• Vision App Deployment

Projects:

✔ Face Detection App
✔ AI Attendance System

PHASE 6: Generative AI & LLM Engineering

Module 7: Generative AI Basics

• What is Generative AI?

• Transformers Architecture

• Self-Attention

• Tokenization

• Prompt Engineering

• Few-shot & Zero-shot

Module 8: LLM Practical Engineering

• Working with LLM APIs

• Embeddings

• Vector Databases

• Fine-Tuning Concepts

• LLM Evaluation

• Guardrails & Safety

PHASE 7: RAG Systems

Module 9: Retrieval-Augmented Generation

• Document Chunking

• Embedding Creation

• Vector Search

• Hybrid Search

• Context Optimization

• Re-ranking

• Hallucination Reduction

Project:

✔ Custom Knowledge AI Assistant

PHASE 8: Agentic AI Engineering

Module 10: AI Agents

• What is Agentic AI?

• ReAct Framework

• Tool Calling

• Memory Systems

• Multi-step Reasoning

• Planning & Reflection

• Multi-Agent Systems

• Autonomous Workflows

• Agent Monitoring

Project:

✔ Autonomous AI Agent

PHASE 9: Deployment & Production

Module 11: AI Deployment

• Flask Deployment

• Streamlit Apps

• REST APIs

• Docker Basics

• Cloud Deployment (Render / Cloud Intro)

• Monitoring & Logging

• Production AI Challenges

Project:

✔ ML + GenAI Deployment

PHASE 10: Industry & Career Preparation

• AI Ethics

• Bias & Fairness

• AI Security

• Portfolio Building

• GitHub Structuring

• Resume Optimization

• Interview Preparation

• System Design Basics

FINAL CAPSTONE TRACK

Students will build:

✔ Spam Detection with Deployment
✔ Vision-based Attendance System
✔ Enterprise RAG System
✔ Autonomous AI Agent
✔ AI SaaS Prototype

Benefits & Outcomes

After completing this course, students will:

• Develop strong AI engineering foundations

• Gain expertise in modern Generative AI technologies

• Build real-world machine learning and AI projects

• Learn to deploy AI applications professionally

• Create a portfolio for AI career opportunities

• Become job-ready for AI and data science roles