• Fundamentals of Computing and Python Basics : Introduction to programming concepts, setting up a development environment (e.g., Jupyter Notebooks).
• Essential Mathematics for AI/ML : Basic linear algebra, calculus, probability, and statistics.
• Data Representation and Visualization : Using Pandas for data manipulation and Matplotlib for data visualization.
• Core AI Concepts and History : Defining AI, early developments, key breakthroughs in AI and ML.
• Introduction to Machine Learning : Understanding AI vs. ML, supervised vs. unsupervised learning, basic Python examples.
• Data Collection and Cleaning : Techniques for gathering and cleaning data for analysis.

• Exploratory Data Analysis (EDA) : Summarizing data, identifying patterns, and performing basic statistical tests.
• Basic Regression and Classification : Understanding and implementing linear regression and nearest neighbor classification.
• Evaluation Metrics and Model Validation : Applying train/test split, accuracy, RMSE, and cross-validation.
• Intro to Ethical AI : Addressing bias in data and responsible AI usage.
• Case Studies : Real-world applications of AI in healthcare, finance, and education.
• Recap of Machine Learning Workflow : Data preprocessing, feature engineering, model training, and evaluation.
• Regression Techniques : Multiple linear regression, polynomial regression, and regularization.
• Classification Algorithms : Logistic regression, decision trees, and ensemble methods.
• Advanced Unsupervised Learning : Clustering, dimensionality reduction, and anomaly detection.
• Neural Network Fundamentals : Perceptrons, activation functions, feed-forward networks, and backpropagation.
• Model Evaluation and Metrics : Precision, recall, F1-score, confusion matrix, and AUC-ROC.
• Techniques for Overcoming Overfitting : Regularization, dropout, data augmentation, and early stopping.
• Hyperparameter Tuning : Grid search, random search, and Bayesian optimization.
• Practical ML Pipeline Development : Building end-to-end ML pipelines and MLOps basics.

• Deep Learning Architectures : Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and sequence models.
• Advanced Neural Network Topics : Batch normalization, residual networks, and transfer learning techniques.
• Natural Language Processing (NLP) : Text preprocessing, word embeddings, and sequence-to-sequence models.
• Transformers and Language Models : BERT, GPT, attention mechanisms, and fine-tuning for tasks like text classification and summarization.
• Computer Vision Beyond Basics : Object detection, image segmentation, and pretrained models.
• Reinforcement Learning Foundations : Markov Decision Processes (MDPs), Q-learning, and policy gradients.
• Generative Models : GANs, autoencoders, and their applications in AI.
• Large-Scale Data Processing and Deployment : Distributed computing, cloud-based AI platforms, and Docker containerization.
• AutoML and Advanced Hyperparameter Tuning : Automated feature engineering and advanced search strategies.
• Frontiers of AI and Research Directions : Ethical AI, fairness in large models, AI governance, and current AI research trends.
• Capstone Project : Integration of multiple advanced AI techniques to develop a robust AI solution.

• Defining Agentic Systems: Core components (planning, reasoning, memory, action).
• The Business Case for Agents: Identifying high-impact scenarios.
• Understanding the LLM Layer: LLMs as the brain for agents.
• Introduction to Frameworks: Overview of LangChain, CrewAI, and AutoGen.

• Advanced Prompt Engineering: Crafting effective system prompts and self-correction.
• The Role of Memory: Implementing short-term and long-term memory.
• Retrieval-Augmented Generation (RAG): Grounding agents in proprietary data.
• Tool Integration (Functions): Connecting agents to external APIs and databases.

• Setting up the Agent Environment: Configuring the development environment.
• Building Your First Agent: Implementing planning, tools, and RAG in a single-agent system.
• Testing and Iteration: Debugging, evaluation, and adaptive feedback loops.
• Real-World Use Cases: Streamlining tasks in finance, HR, and content creation.

• Designing Multi-Agent Workflows: Creating teams of specialised agents and sequential tasks.
• Framework Deep Dive: Hands-on practice with CrewAI and AutoGen roles and communication.
• Deployment and Monitoring: Strategising deployment, observability, and logging.
• Governance and Ethical Scaling: Protocols for ethical use and data privacy.
• No-Code Agent Builders: Introduction to n8n, make.com, and ChatGPT agents.

• Full-Cycle Agent Design: Problem definition, tool identification, and memory structure.
• Prototyping a Multi-Agent System: Building a functional system using LangChain, CrewAI, or AutoGen.
• Strategic Pitch: Creating a business case with ROI, ethics, and deployment details.

• Introduction to Artificial Intelligence
• Industrial applications of AI
• Bias and fairness in AI Systems

• Descriptive Statistics and Exploratory Data Analytics
• Statistical Inferences
• Linear Regression
• Logistic Regression Analysis
• Clustering and Introduction to Unsupervised Learning

• Sentiment Analysis
• Topic Modelling

Predict Attrition & Optimize Hiring

• Designing Interactive Dashboards on PowerBI
• HR metrics and Dashboards
• Workforce Predictive Modelling
• Turnover Predictive Modelling
• Managing Employee Performance using Analytics

Customer Segmentation & ROI

• Introduction to marketing analytics
• Segmentation and Positioning
• Product analytics
• Marketing mix modelling
• Consumer analytics

Supply Chain Forecasting

• Fundamentals of Operation Analytics
• Retail and e-commerce operations
• Product Process Matrix
• Theory of Constraints
• Quality Management (Lean & Six-Sigma)
• Forecasting

• The Data Landscape: Understanding different types of data and the data lifecycle
• Excel Essentials: Cleaning, structuring, and analyzing data using basic spreadsheet
• Python for Analytics: Mastering core Python syntax, variables, and data structures
• SQL Fundamentals: Basic querying and managing data in a relational database
• The AI Ecosystem: A high-level, non-technical overview of AI, Machine Learning, and how Generative AI fits into this picture

• What is an LLM?: Understanding the basic architecture and capabilities of models like GPT and Gemini
• Prompt Engineering Techniques: Mastering core prompting strategies, from zero-shot to few-shot prompting
• AI-Assisted Writing & Analysis: Using LLMs to summarize articles, generate reports, and assist with creative tasks
• Understanding Tokens: A conceptual overview of how models process information.
• Exploring Open-Source LLMs: An introduction to publicly available models and their use cases
• Interacting with the API: A simple introduction to calling the OpenAI API with Python

• Data Cleaning with AI: Leveraging AI tools to automatically find and fix errors in datasets
• Exploratory Data Analysis (EDA): Using Numpy and Pandas to generate initial hypotheses and summarize key statistics from your data
• AI-Powered Visualization: Creating stunning and informative charts and graphs with the help of AI

• The Anatomy of an AI Agent: Understanding the core components of autonomous agents, including planning, memory, and tool use
• Building with Frameworks: Getting hands-on with popular frameworks like LangChain and CrewAI to design and build multi-agent systems
• AI Automation & Tool Use: Creating agents that can interact with APIs and online resources to perform tasks like research

• In this module, you will design and build a Capstone Project from scratch, showcasing your ability to apply Generative and Agentic AI principles to a real-world problem.

• Introduction to product management and its lifecycle
• Understanding customer needs and market research techniques
• Defining product vision, mission, and strategy
• Key metrics and KPIs in product management

• Advanced product development and roadmap planning
• Managing cross-functional teams and stakeholder communication
• Scaling products and optimizing customer experiences
• Navigating complex product challenges and decision-making

• Strategic analysis and competitive advantage
• Market segmentation and target audience identification
• Business models, revenue streams, and cost structures
• Growth hacking techniques and sustainable scaling strategies

• Understanding AI technologies and their impact on products
• Integrating AI solutions into product management
• Building AI-driven product features and functionalities
• Ethics and challenges of implementing AI in products

• Introduction to agentic AI and its potential applications
• Ethical considerations in AI development and deployment
• Addressing bias, fairness, and accountability in AI
• Building responsible AI systems with a human-centered approach

• Applying learning to design an AI-driven product
• Conducting market validation and feasibility studies
• Crafting a compelling pitch for stakeholders and investors
• Final presentation and product showcase

1.1 Introduction to Cybersecurity Fundamentals
• Core concepts: Confidentiality, Integrity, Availability (CIA Triad)
• Threat actors & attack vectors
• Key principles: Defense-in-Depth, Least Privilege, Zero Trust
• Frameworks & standards: NIST, ISO 27001 (overview), CIS Controls
1.2 Networking Essentials for Security
• TCP/IP model & key protocols
• Network devices and their security roles
• Topologies, segmentation & wireless security
• Tools: Wireshark, tcpdump
• Lab: Network-traffic analysis with Wireshark
1.3 Understanding the Evolving Threat Landscape
• Current & emerging cyber-threat trends
• Impact of new technologies on security
• Threat-intelligence fundamentals
• Lab: Case-study analysis of a recent cyber-attack

2.1 Ethical Hacking Methodologies
• Phases of ethical hacking
• Legal & ethical considerations
• Intro to penetration-testing toolsets
• Tools: Kali Linux
• Lab: Setting up Kali VM & vulnerable targets
2.2 Footprinting & Reconnaissance Techniques
• Passive reconnaissance (OSINT)
• Active reconnaissance: scanning & enumeration
• Tools: Nmap, Shodan, theHarvester, dig / nslookup
• Lab: Comprehensive OSINT + active recon
2.3 Vulnerability Assessment Strategies
• Vulnerability-management lifecycle
• Scanning tools & workflows
• CVE / CVSS basics; spotting common vulns
• Tools: OpenVAS, Nessus (trial)
• Lab: Run a vulnerability scan & interpret results

3.1 Advanced Network-Security Controls
• Firewalls deep dive: WAFs, NGFWs
• IDS / IPS: signature vs. anomaly detection
• VPNs, NAC, DDoS mitigation
• Tools: Snort, Wazuh, iptables
• Lab: Configure firewall rules & analyse IDS/IPS alerts
3.2 Security Operations Centre (SOC) & SIEM
• SOC roles, tiers & workflows
• SIEM basics: log collection, correlation, alerting
• Tools: Splunk (community), ELK Stack
• Lab: Security-log analysis inside a SIEM
3.3 Comprehensive Incident Response
• Incident-response lifecycle & planning
• Digital-forensics basics for IR
• Effective incident communication
• Lab: Build a basic IR playbook

4.1 Secure-SDLC Integration & Threat Modelling
• Embedding security in each SDLC phase
• STRIDE threat-modelling workshop
• Security-by-design principles
• Lab: Build a simple threat model
4.2 Web & API Vulnerabilities (OWASP)
• OWASP Top 10 deep dive
• API-security best practices
• Tools: Burp Suite (community), OWASP ZAP, SQLMap
• Lab: Exploit common web/API vulns in a safe environment
4.3 Secure Coding Best Practices
• Data protection & cryptography essentials
• Secure configuration, error handling & logging
• Dependency / supply-chain security
• Tools: Bandit, Snyk (SCA)
• Lab: Code review & remediation exercise

5.1 Leveraging AI/ML for Cybersecurity
• Fundamentals of ML in security contexts
• Use-cases: anomaly detection, malware classification, etc.
• Data requirements & pitfalls
• Tools: scikit-learn, TensorFlow (basic)
• Lab: Train a simple malware-detection model
5.2 Security Automation & Orchestration with AI
• SOAR platform concepts
• Automated incident triage & response
• AI-driven vulnerability prioritisation
• Tools: Cortex (concepts), TheHive, Python scripting
• Lab: Design an automated security workflow
5.3 Adversarial AI & Defensive Techniques
• How attackers exploit AI/ML
• Basics of adversarial machine-learning (AML)
• Defensive counter-measures
• Tools: IBM ART, Google AutoML (conceptual)
• Lab: Identify & discuss adversarial examples

Work on projects like
• Cybersecurity Foundations (Advanced Refresh)
• Compliance Gap Analysis Report

Business problem framing, KPIs, Excel modelling, basic statistics, MySQL, distributions, hypothesis testing, A/B testing.

Visualization Best Practices & Perception, Choosing the Right Chart, Introduction to Tableau and Power BI, Building Interactive Dashboards, Data Storytelling Frameworks, Advanced Visualization Techniques.

Data is the foundation for Artificial Intelligence models. Generative AI models rely on large amounts of high-quality data to learn patterns, relationships, and structures. Understanding various data stores helps students appreciate how data is stored, managed, and retrieved. This data is usually stored in a variety of data stores viz. relational databases, NoSQL databases, data lakes. Knowledge of these data stores will enable students process various data formats

Learn basic Python required for GenAI- Data Types, Flow Structures, Data Structures, Functions, Libraries, Python in Data Science.

Supervised and unsupervised learning, time series analysis, churn prediction, forecasting. Examples include sentiment analysis for customer reviews.

Understand the transition from traditional machine learning to neural networks and deep learning and their common applications.

You can use LLM for content creation, computer programming, problem solving, dataset generation, and learning. In this model, you will learning how to effectively leverage LLM to improve your productivity. You will be learning basic prompting strategies such as setting the context, role prompting, and tuning model settings. You will also learn advanced prompting methods such as chain-of-thought (COT) prompting.

Supervised and unsupervised learning, time series analysis, churn prediction, forecasting.

• Learn core linear algebra concepts like vectors, matrices, echelon form, and solving systems of linear equations.
• Understand vector spaces, basis, linear transformations, and projections crucial for AI and ML models.
• Build calculus foundations including functions, gradients, partial derivatives, and the Jacobian and Hessian matrices.
• Grasp key probability concepts such as conditional probability, Bayes’ theorem, random variables, and joint distributions.

• Introduce core supervised learning concepts, including regression and classification.
• Cover essential unsupervised learning methods, particularly clustering and dimensionality reduction.
• Introduce ensemble methods and model selection strategies.
• Provide a strong basis in evaluation metrics and best practices for validating machine learning models.

• Master the fundamentals of deep neural networks and advanced architectures (CNNs, RNNs, transformers).
• Understand and implement generative models (VAEs, GANs) for creative AI applications.
• Explore specialized AI topics: NLP, computer vision, and reinforcement learning.
• Develop insight into research frontiers and future directions in AI.