Become an Industry-Ready Generative AI for Developers
To empower learners to build AI-powered applications using generative models and LLMs (Large Language Models), applying prompt engineering, API integration, and model fine-tuning techniques.
- Academic partner with UGC, AICTE, and NCVET alignment
- 200+ programs across domains
- 1,000+ industry partners and global collaborations
Program Objective
To empower learners to build AI-powered applications using generative models and LLMs (Large Language Models), applying prompt engineering, API integration, and model fine-tuning techniques.
Key Features
Curriculum Structure
Module 1 - Generative AI Concepts & Model Architectures
Topics Covered:
Introduction to Generative AI
Evolution of AI: From ML to Generative Models
Large Language Models (LLMs) Overview
Transformers Architecture
Diffusion Models & GANs Basics
Text, Image, and Code Generation Models
Model Training Fundamentals
Module 2 - Prompt Engineering & Fine-Tuning
Topics Covered:
Fundamentals of Prompt Engineering
Zero-shot, One-shot & Few-shot Prompting
Chain-of-Thought Prompting
Role-based & Structured Prompts
Prompt Optimization Techniques
Fine-Tuning vs. Prompt Engineering
Introduction to LoRA & Parameter-Efficient Fine-Tuning
Module 3 - APIs & Frameworks for LLM Integration
Topics Covered:
Working with LLM APIs
REST API Basics for AI Integration
Introduction to LangChain
Vector Databases & Embeddings
Retrieval-Augmented Generation (RAG)
Integrating LLMs into Web Applications
Deployment Basics
Module 4 - Building Generative Applications (Text, Image, Code) Module 5 - Responsible AI & Model Governance
Topics Covered:
Building AI Chatbots
Text Summarization & Content Generation Apps
Image Generation Applications
AI Code Assistants
Multi-modal Applications
UI/UX Considerations for AI Apps
Performance Optimization
Module 5 - Responsible AI & Model Governance
Topics Covered:
AI Ethics & Bias
Data Privacy & Security
Hallucinations in LLMs
Model Explainability
Governance Frameworks
Risk Assessment & Mitigation
Regulatory Overview
Module 6 - Capstone Project: Generative AI Application
Topics Covered:
Problem Statement Selection
Project Planning & Architecture
Model Selection & Integration
Frontend + Backend Implementation
Testing & Validation
Deployment & Presentation
Documentation & Demo
Module 7 - Employability & Professional Skills
Topics Covered:
Resume Building for AI Roles
GitHub Portfolio Development
LinkedIn Optimization
Interview Preparation (Technical + HR)
Communication & Presentation Skills
Industry Case Studies
Career Pathways in Generative AI
Module 1 - Supervised Machine Learning Algorithms
Topics Covered:
- Linear & Logistic Regression
- Decision Trees & Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Model evaluation metrics
- Hyperparameter tuning
- Building end-to-end ML pipelines
Module 2 - Unsupervised Learning & Pattern Recognition
Topics Covered:
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction (PCA)
- Anomaly detection
- Association rules
- Customer segmentation (use case)
- Evaluating unsupervised models
- Visualization for cluster insights
Module 3 - Deep Learning & Neural Networks (DL Basics)
Topics Covered:
- Understanding neural network architecture
- Forward & backward propagation
- Activation functions
- Loss functions & optimizers
- Creating neural networks with TensorFlow/Keras
- Underfitting vs overfitting
- Model regularization
- DL experimentation workflows
Module 4 - Computer Vision (CV) Fundamentals
Topics Covered:
- Image processing basics
- Convolutional Neural Networks (CNNs)
- Pooling, padding, filters
- Image classification
- Data augmentation
- Transfer learning (VGG, ResNet)
- Building CV projects using Keras
- Model deployment
Module 5 - Natural Language Processing (NLP)
Topics Covered:
- Text preprocessing: tokenization, stemming, lemmatization
- Bag of Words & TF-IDF
- Sentiment analysis
- Intro to word embeddings
- Text classification use cases
- Building NLP models with Scikit-learn
- Deploying NLP models
- Real-world NLP workflows
Module 1 - Product Thinking for AI & ML Solutions
Topics Covered:
- Problem identification using ML
- Understanding business metrics
- Mapping data to product features
- Building user-centric AI solutions
- Figma for AI product UI
- Model lifecycle in products
- Documentation & ML requirements
Module 2 - Startup Innovation Using AI & ML
Topics Covered:
- AI product idea generation
- Competitive analysis
- Building MVPs for ML products
- AI monetization strategies
- Creating pitch decks
- Using AI tools for product research
- Preparing go-to-market (GTM) plans
Module 3 - Professional Development for ML Careers
Topics Covered:
- Resume building for AI/ML profiles
- LinkedIn optimization
- Writing ML case studies
- GitHub portfolio for ML projects
- Communication skills for data storytelling
- Interview preparation (ML + coding)
Mini Projects
- Sales prediction using Regression
- Iris dataset classifier
- Customer segmentation using K-means
- Sentiment analysis on social media data
Concepts Covered:
- EDA
- ML basics
- Clustering
- Classification
Intermediate Projects
- Image classification with CNN
- Spam detection (NLP)
- Credit card fraud detection
- Housing price prediction
Concepts Covered:
- Deep learning
- NLP
- Evaluation metrics
- Optimization
Capstone Projects
- End-to-End ML Pipeline & Deployment
- AI Customer Insights Platform
- NLP-powered Recommendation System
- Deep Learning Visual Recognition System
Concepts Covered:
- Architecture design
- Deployment
- Experimentation
- Documentation
Tools & Softwares
Program Outcomes
Salary Scale
Career Roles
- Generative AI Developer
- AI Application Engineer
- Prompt Engineer
- AI Automation Specialist
- LLM Integration Developer
FAQ's
Yes. You'll receive VTU-compliant certificates and documentation.
No. The program starts from fundamentals and scales gradually.
Prediction models, CNN-based classifiers, NLP applications, and a full ML pipeline.
Yes — resume, LinkedIn, GitHub, mock interviews & job guidance.
Offered in both offline and hybrid formats.
Yes, you will receive a verified completion certificate from Rooman Technologies upon meeting all requirements.
CSE, ISE, AIML, ECE, EEE, Civil, Mechanical and all final-year VTU students.
Python, Scikit-learn, TensorFlow, Pandas, Kaggle workflows, cloud ML basics.
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000
Send us a Message
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000