Become an Industry-Ready AI Foundations
To provide learners with the skills required to plan, manage, and optimize online business operations including product cataloging, logistics, customer service, and digital marketing within e-commerce ecosystems.
- Academic partner with UGC, AICTE, and NCVET alignment
- 200+ programs across domains
- 1,000+ industry partners and global collaborations
Program Objective
To introduce learners to the core principles, technologies, and applications of Artificial Intelligence (AI), preparing them to understand and apply AI concepts across various domains.
Key Features
Curriculum Structure
Module 1 - Introduction to Artificial Intelligence
Topics Covered:
Overview of Artificial Intelligence & its real-world applications
Evolution of AI and societal impact
Introduction to Responsible & Ethical AI
Key principles: Fairness, Transparency, Accountability, Privacy, Safety
Human-Centered AI design approach
AI risks, failures & global case studies
Module 2 - Machine Learning Concepts & Algorithms
Topics Covered:
Overview of Machine Learning lifecycle
Sources of bias in datasets and algorithms
Types of bias: historical, sampling, measurement, algorithmic
Fairness metrics and evaluation techniques
Bias detection tools & mitigation strategies
Inclusive AI system design
Module 3 - Neural Networks & Deep Learning Basics
Topics Covered:
Foundations of Neural Networks
Artificial Neurons & Perceptron Model
Activation Functions
Forward & Backpropagation
Deep Learning Fundamentals
Types of Neural Networks (FNN, CNN, RNN)
Module 4 - AI Tools & Frameworks (AWS, TensorFlow, Scikit-learn)
Topics Covered:
Introduction to AI Development Environments
Overview of AWS AI & ML Services
Amazon SageMaker Basics
Introduction to TensorFlow
Introduction to Scikit-learn
Model Deployment Concepts
MLOps Fundamentals
Monitoring & Model Maintenance
Module 5 - Ethics, Bias & Responsible AI
Topics Covered:
Foundations of AI Ethics
Types of Bias in AI Systems
Fairness Metrics
Bias Detection & Mitigation Techniques
Explainable AI (XAI) Concepts
Data Privacy & Protection Principles
AI Governance Frameworks
Responsible AI Best Practices
Module 6 - Capstone Project: AI Use Case Implementation
Topics Covered:
Problem Statement Identification
Data Collection & Preprocessing
Model Selection & Development
Model Training & Evaluation
Bias & Risk Assessment
Deployment Strategy
Project Documentation & Presentation
Module 7 - Employability & Professional Skills
Topics Covered:
Career Pathways in AI & ML
Resume & Portfolio Building
GitHub & Project Showcasing
Interview Preparation (Technical & HR)
Case Study Discussions
Communication & Stakeholder Skills
Professional Ethics in AI Careers
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
- AI Intern
- Data Analyst (AI Applications)
- Junior Machine Learning Engineer
- AI Research Assistant
- AI Support Executive
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