Become an Industry-Ready AI in Business Strategy
To prepare managers and aspiring leaders to understand and integrate Artificial Intelligence (AI) into business strategy, driving data-informed decision-making and digital transformation.
- Academic partner with UGC, AICTE, and NCVET alignment
- 200+ programs across domains
- 1,000+ industry partners and global collaborations
Program Objective
Key Features
Curriculum Structure
Module 1 - AI in Business Transformation: An Overview
Topics Covered:
- Introduction to Responsible & Ethical AI
The role of AI in modern business transformation
Risks of unethical AI in enterprises
Trust, transparency & competitive advantage
Global trends in AI regulation and governance
Case studies of AI success & failures
Module 2 - Strategic Management & AI Integration
Topics Covered:
Embedding Responsible AI into business strategy
AI risk assessment frameworks
Aligning AI initiatives with organizational values
Stakeholder mapping & impact analysis
Ethical decision-making in leadership
Building cross-functional AI governance teams
Module 3 - AI Business Models & Digital Transformation
Topics Covered:
Ethical AI-driven business models
Sustainable & trustworthy AI innovation
Digital transformation with governance controls
AI maturity models & readiness assessment
Responsible AI adoption roadmap
Competitive differentiation through ethical AI
Module 4 - Data Analytics for Decision-Making
Topics Covered:
Data governance frameworks
Bias detection in business analytics
Responsible data collection & management
Privacy-preserving data strategies
AI performance monitoring & risk dashboards
Transparent reporting mechanisms
Module 5 - Ethics, Governance & Responsible AI Strategy
Topics Covered:
Designing Responsible AI policies
Regulatory compliance overview (GDPR, AI Act basics)
Accountability frameworks
Model risk management
Ethical impact assessments
Human-in-the-loop governance systems
Module 6 - Capstone Project: AI Strategy Proposal
Topics Covered:
Identify a real-world AI use case
Conduct ethical & risk assessment
Develop bias mitigation strategy
Design governance & compliance framework
Create a Responsible AI implementation roadmap
Present a strategic AI proposal to a mock leadership board
Module 7 - Employability & Professional Skills
Topics Covered:
Career opportunities in Responsible AI
Leadership & ethical decision-making skills
Business communication & stakeholder management
Resume & portfolio building
Case study discussions & interview preparation
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 Strategy Consultant
- Business Transformation Manager
- Digital Strategy Analyst
- Innovation Manager
- Data-Driven Decision Specialist
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