Curriculum

The Fourth Industrial Revolution (Industry 4.0) is driven by transformative digital technologies such as Python programming, database management systems (SQL & NoSQL), machine learning, prompt engineering, advanced Git & GitHub workflows, and API development & integration, which together form the foundation of intelligent software engineering. As learners progress, they gain expertise in MLOps, deep learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, AI engineering & testing, and containerization using Docker, enabling the development of scalable, production-ready AI applications. The curriculum further extends to SAP Developer Skills, SAP Analytics Cloud, cloud computing fundamentals, edge computing & embedded AI systems, empowering students to build enterprise-grade, cloud-native, and intelligent solutions. Complemented by design thinking, capstone projects, and employability skills, these technologies prepare students to innovate and excel in the rapidly evolving digital economy.

The Centres of Excellence established by Edunet Foundation in collaboration with SAP across selected engineering colleges in Telangana, and Karnataka provide students with industry-aligned skilling opportunities through the Code Unnati 5.0 program. The curriculum covers a comprehensive learning pathway beginning with Python Programming, Database Management Systems, Machine Learning, Prompt Engineering, Advanced Git & GitHub Workflows, and API Development & Integration, followed by advanced modules in MLOps, Deep Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and AI Engineering & Testing. For final-year students, the program offers specialized training in SAP Developer Skills, SAP Analytics Cloud, Cloud Computing Fundamentals, and Edge Computing & Embedded AI Systems, along with hands-on capstone projects and structured employability training. This holistic approach equips students with industry-relevant technical expertise, practical project experience, and professional competencies, enabling them to secure internships, excel in campus placements, and build successful careers in AI, enterprise technologies, and cloud-driven digital transformation.

The program curriculum caters to the 2nd, 3rd, and 4th year students from the partner engineering colleges as summarized below:

The program curriculum caters to the 2nd, 3rd, and 4th year students from the partner engineering colleges as summarised below:

Content Type Duration (Hours) Delivery Type Delivery Mode
2nd Year 3rd/4th Year 4th Year
1 Core Deep Tech Offering ~85 ~95 ~60 Instructor led Hybrid
2 Employability Skills ~15 ~20 ~15 Instructor led Hybrid
3 Capstone Project ~20 ~30 ~60 Instructor led Hybrid
Year-wise Total Hours 120 145 135

II Year — Foundation Course

The Foundation Course is a meticulously designed 120-hour intensive program tailored for B.Tech 2nd year students. It aims to bridge the gap between academic curriculum and industry expectations by building strong foundational technical skills in programming, databases, modern AI tools, machine learning, version control, API development, and professional readiness. This program emphasizes hands-on learning, project-based application, and AI-augmented development using tools like GitHub Copilot and ChatGPT. Through structured modules, multiple mini-projects, and a culminating Capstone Project guided by Design Thinking principles, students will develop job-ready competencies that enhance their technical confidence and employability in the rapidly evolving tech landscape. The course integrates traditional programming and database concepts with cutting-edge practices such as Prompt Engineering, Advanced Git & GitHub Workflows, and API Development & Integration, ensuring students are well-prepared for internships, placements, and future advanced studies in Computer Science and related fields.

Course Objectives

The primary objectives of this program are:

  • Establish a solid foundation in Python programming and Object-Oriented Programming (OOP) principles.
  • Develop strong expertise in Database Management Systems, covering both SQL and NoSQL technologies.
  • Introduce core concepts of Machine Learning, including data preprocessing, modeling, evaluation, and unsupervised learning basics.
  • Master Prompt Engineering techniques to effectively leverage Generative AI tools for coding, problem-solving, and workflow automation.
  • Build proficiency in Advanced Git & GitHub Workflows for collaborative software development and CI/CD practices.
  • Gain practical skills in API Development & Integration using modern frameworks and authentication mechanisms.
  • Apply Design Thinking methodology through a comprehensive Capstone Project.
  • Enhance overall employability by integrating technical skills with professional development and industry-relevant practices.
Program Benefits

The program offers numerous benefits that will significantly benefit B.Tech 2nd year students:

  • Industry-Aligned Skill Development: Gain practical, job-relevant skills in high-demand areas such as Python, Databases, AI tools, and Machine Learning.
  • AI-Powered Productivity: Learn to use modern AI assistants (GitHub Copilot, ChatGPT) to accelerate coding, debugging, and documentation.
  • Strong Project Portfolio: Build Mini Projects + 1 Major Capstone Project that can be showcased during internships and placements.
  • Enhanced Problem-Solving Abilities: Develop logical thinking, data handling, and analytical skills through real-world problem scenarios and design thinking.
  • Improved Employability: Dedicated module on soft skills, resume building, interview preparation, and industry awareness to boost placement readiness.
  • Holistic Learning Experience: Balanced mix of theory, intensive practical sessions, collaborative tools, and guided projects under structured mentorship.
Skill Set

Upon successful completion of the program, students will have developed the following key skill sets:

  • Python Programming Mastery: Core Python concepts, data structures, OOP principles, file handling, and exception management. Ability to write clean, efficient, and modular code.
  • Database Management Expertise: Proficient in SQL and NoSQL databases. Hands-on experience with schema design, CRUD operations, dimensional modeling, PyMongo, and mapping relational models to NoSQL structures.
  • Machine Learning Fundamentals: Data preprocessing, Exploratory Data Analysis (EDA), and feature engineering. Implementation of supervised and unsupervised learning.
  • Prompt Engineering & Generative AI: Advanced prompting techniques (Zero-shot, Few-shot, Chain-of-Thought, ReAct, Role Prompting, Self-Consistency). Effective use of AI tools like GitHub Copilot and ChatGPT for code generation, debugging, optimization, documentation, and collaborative development on platforms. Understanding of data fundamentals, quality, validation, and bias considerations in AI workflows.
  • Advanced Git & GitHub Workflows: Git architecture, CLI fundamentals, branching strategies (GitFlow), pull requests, code reviews, merge conflict resolution, rebase vs. merge, and GitHub Actions with security practices.
  • API Development & Integration: RESTful architecture, building APIs with FastAPI/Flask, data validation, authentication & authorization, third-party integrations, and API documentation/testing with Swagger/OpenAPI and Postman.
  • Project Development & Design Thinking: End-to-end project execution integrating Python, Databases, AI tools, and ML models, Git & API’s. Application of Design Thinking stages (Empathize, Define, Ideate, Prototype, Test) with user research, prototyping, usability testing, and iterative feedback.
  • Employability & Professional Skills: Technical communication, collaborative development practices, industry-aligned project experience, and placement readiness.

Program Outline

Module No Module Name Estimated Hours
1 Python Fundamentals 15
2 Database Management Systems 16
3 Machine Learning Basics 16
4 Prompt Engineering 15
5 Advanced Git & GitHub Workflows 11
6 API Development & Integration 12
7 Capstone Project 20
8 Employability Skills Course 15
Total Duration 120

Detailed Program Outline

01Python Programming Language

Python Fundamentals

15 Hours
  • Introduction to Python
  • Data Types & Variables
  • Operators & Control Flow
  • Functions, Modules & Packages
  • Exception Handling
  • File Handling
  • Classes & Objects - Constructors, Attributes & Methods
  • Basic OOP Principles (Encapsulation, Inheritance, Polymorphism)
  • Python Standard Libraries
04Database Management Systems

Database Management Systems

16 Hours
  • Introduction to DBMS
  • Relational Database Concepts
  • SQL Basics
  • Advanced SQL
  • Star Schema & Dimensional Modeling
  • SQL vs NoSQL Comparison
  • PyMongo Implementation & CRUD Handling
  • Mapping Relational Models with NoSQL Structures
  • NoSQL Databases (MongoDB / Redis / Cassandra)
03Data Structures and Algorithms

Machine Learning Basics

16 Hours
  • Introduction to Machine Learning
  • Types of ML (Supervised, Unsupervised, Reinforcement)
  • Machine Learning Workflow
  • Data Preprocessing & Cleaning
  • Exploratory Data Analysis (EDA)
  • Statistical Analysis & Data Visualization
  • Feature Engineering
  • Supervised Learning (Regression)
  • Model Evaluation Metrics (MSE, RMSE, R², etc.)
  • Supervised Learning (Classification)
  • Classification Evaluation Metrics
  • Introduction to Unsupervised Learning
02Object-Oriented Programming and Software Design

Prompt Engineering

15 Hours
  • Introduction to Prompt Engineering
  • Core Prompting Techniques
  • Zero-shot, Few-shot and Chain-of-Thought
  • Advanced Prompting Methods like ReAct
  • Role Prompting and Self-Consistency
  • Structuring Prompts for Code Generation and Analysis
  • AI-Assisted Coding & Workflow Tools using GitHub Copilot and ChatGPT
  • AI-Assisted Workflows for Debugging, Code Optimization, Documentation and Analysis
  • Practical Exposure to Development Platforms (Replit)
  • Introduction to Data Fundamentals
  • Data Quality and Validation
  • Concepts of Bias in Training Data
  • Best Practices for Prompting
05Introduction to Competitive Coding

Advanced Git & GitHub Workflows

11 Hours
  • Git Architecture & CLI Fundamentals
  • Branching Strategies & GitFlow
  • Pull Requests
  • Code Reviews & Collaboration
  • Merge Conflicts
  • Resolution & Rebase vs. Merge
  • GitHub Actions Basics & Security
06Data Science and Analytics

API Development & Integration

12 Hours
  • RESTful Architecture & HTTP Methods
  • Building APIs
  • Data Validation
  • API Consumption & Third-Party Integrations
  • Authentication & Authorization
  • API Documentation
  • Postman Testing
07Capstone Project

Capstone Project

20 Hours
  • Capstone Project is based on learning and students create prototype level solutions for real life problems.
  • Design Thinking – Way to solve problems with creative thinking.
08Data Science and Analytics

Employability Skills Course

15 Hours
  • Foundational Activities for Communication Readiness

* Note – Final assessment and Capstone Project will be criteria for Course completion certificate.

III Year — Advance Course

The Advanced Course is a meticulously designed 145-hour intensive program that builds upon foundational technical skills to equip B.tech 3rd year students with advanced competencies in modern AI systems, MLOps, deep learning, generative AI, and agentic workflows. It aims to bridge the gap between intermediate programming knowledge and industry-ready expertise in deploying, scaling, and engineering intelligent applications. This program emphasizes hands-on learning, project-based application, and end-to-end AI system development. Through structured modules covering MLOps & Containerization, Deep Learning, Generative AI (LLMs & RAG), Agentic AI, AI Engineering & Testing, a comprehensive Capstone Project guided by Design Thinking, and dedicated Employability Skills, students will develop production-grade capabilities that significantly enhance their technical confidence and employability in the rapidly evolving AI landscape. The course integrates cutting-edge practices such as model deployment, containerization with Docker, large language model fine-tuning, multi-agent systems, rigorous AI testing, and professional communication, ensuring students are well-prepared for advanced internships, placements, and specialized roles in AI engineering and applied machine learning.

Course Objectives

The primary objectives of this program are:

  • Master MLOps practices including model versioning, experiment tracking, CI/CD for ML, monitoring, and containerized deployment using Docker.
  • Build and train deep learning models using TensorFlow or PyTorch, covering FNNs, CNNs, RNNs, LSTMs, and techniques to handle overfitting and vanishing gradients.
  • Develop expertise in Generative AI, Large Language Models, Transformer architecture, fine-tuning methods, RAG systems, and end-to-end LLM application development.
  • Design and implement Agentic AI systems with tool integration, multi-agent collaboration, memory management, and autonomous workflows using frameworks like LangChain, LangGraph, and CrewAI.
  • Apply rigorous AI Engineering & Testing practices including unit/integration testing, LLM evaluation frameworks, token optimization, guardrails, and production safety.
  • Execute a comprehensive Capstone Project using Design Thinking methodology to solve real-world problems.
  • Strengthen professional communication, presentation, and workplace readiness skills.
Program Benefits

The program offers numerous benefits that will significantly enhance students’ career prospects:

  • Industry-Aligned Advanced Skill Development: Gain practical expertise in high-demand areas such as MLOps, Deep Learning, Generative AI, Agentic Systems, and AI Engineering.
  • Production-Ready AI Capabilities: Learn to deploy, containerize, monitor, and scale ML/LLM models using industry-standard tools and workflows.
  • Strong Project Portfolio: Build multiple Mini Projects + 1 Major Capstone Project demonstrating end-to-end AI system design and deployment.
  • Enhanced Problem-Solving & System Design Abilities: Develop skills in building autonomous agents, optimizing LLM pipelines, and ensuring reliable AI systems in production.
  • Improved Employability: Dedicated 20-hour Employability Skills module covering communication, presentation, telephonic skills, and professional orientation to boost placement readiness.
  • Holistic Advanced Learning Experience: Balanced mix of theory, intensive practical sessions, modern frameworks, collaborative tools, and guided real-world projects under structured mentorship.
Skill Set

Upon successful completion of the program, students will have developed the following advanced skill sets:

  • MLOps & Containerization: Git & Github, Rest API, Model versioning, experiment tracking, ML pipelines, CI/CD for ML, model monitoring, and data drift handling. Containerization using Docker, Dockerfile creation, and deployment of ML models in production environments.
  • Deep Learning Proficiency: Neural network architectures (FNNs, CNNs, RNNs, LSTMs), backpropagation, optimization techniques, and regularization. Hands-on model building and training using TensorFlow and PyTorch frameworks.
  • Generative AI, LLMs & RAG: Transformer architecture, LLM application development, token management, and context handling. Retrieval Augmented Generation (RAG) pipelines, vector databases, chunking strategies, and RAG optimization.
  • Agentic AI Development: Building autonomous AI agents, prompt chaining, tool integration, multi-agent collaboration, and memory management. Working with frameworks like LangChain, LangGraph, CrewAI, and no-code agent platforms.
  • AI Engineering & Testing Competence: Unit & integration testing with PyTest, prompt testing for non-deterministic systems, LLM evaluation, token optimization, cost & latency reduction, automated guardrails, and safety/error handling for production AI systems.
  • Design Thinking & Capstone Execution: Application of Empathize, Define, Ideate & Prototype Test stages, user research, prototyping, usability testing, and iterative improvement on complex AI solutions.
  • Professional & Employability Skills: Effective technical communication, self-introduction, presentation skills, telephonic etiquette, interview preparation, and collaborative teamwork. Internship Readiness, Professional Branding & Digital Presence, Interview Intelligence & Hiring Simulation, Project Storytelling & Presentation Mastery, Group Discussion & Collaborative Communication.
Prerequisites
  • Basic understanding of problem solving.
  • Introductory understanding of Python programming.
  • Understanding of Data Analytics Concept.
  • Elementary concepts of Probability theory, Statistics & Continuous variable Calculus.

Program Outline

Module No Module Name Estimated Hours
1 MLOps & Containerization 30
2 Deep Learning 18
3 Generative AI, LLMs and RAG 16
4 Agentic AI 18
5 AI Engineering & Testing 13
6 Capstone Project 30
7 Employability Skills Course 20
Total Duration 145

Detailed Course Outline

01MLOps & Containerization

MLOps & Containerization

30 Hours
  • Git & GitHub Basics
  • Branching and Pull Requests
  • Working with Remote Repositories
  • REST APIs Development
  • Building and Exposing ML Models as APIs
  • Beginner-Friendly Deployment Workflow
  • Introduction to MLOps
  • Model Versioning
  • Experiment Tracking
  • ML Pipelines
  • CI/CD for ML
  • Model Monitoring & Data Drift Handling
  • Deployment Strategies
  • Introduction to Containers & Virtualization
  • Docker Installation & Configuration
  • Docker Images & Containers
  • Dockerfile Creation and Compose
  • Deploying ML Models using Docker
  • Mini Project Deployment on Cloud Platforms
02Deep Learning

Deep Learning

18 Hours
  • Neural Network Basics
  • Gradient Descent and Optimization
  • Backpropagation & Activation Functions
  • Challenges – Vanishing and Exploding Gradients
  • Introduction to TensorFlow & PyTorch
  • Building and Training Models using Frameworks
  • Feedforward Neural Networks (FNNs)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) and LSTMs
  • Overfitting, Underfitting and Regularization Techniques
  • Transfer Learning Basics
  • Model Performance Evaluation
03Generative AI, LLMs and RAG

Generative AI, LLMs and RAG

16 Hours
  • Introduction to Generative AI
  • Large Language Models Fundamentals
  • Transformer Architecture
  • Major LLM Families
  • Data Preparation for LLMs
  • Training LLMs from Scratch
  • Fine-tuning LLMs
  • Model Evaluation and Benchmarks
  • Implementing Transformers from Scratch
  • Model Optimization and Compression
  • End-to-End LLM Application Development
04Agentic AI

Agentic AI

18 Hours
  • Introduction to Agentic AI
  • Building Simple Task-Oriented AI Agents
  • Prompt Chaining
  • Tool Integration and Function Calling
  • Workflow-Based Automation
  • No-Code Agent Platforms
  • Code-Based Agent Frameworks
  • Multi-Agent Systems
  • Short-Term and Long-Term Memory
  • Autonomous Workflow Design
  • Error Handling and Guardrails
05AI Engineering & Testing

AI Engineering & Testing

13 Hours
  • Unit & Integration Testing with PyTest
  • Prompt Testing
  • Determinism in Non-Deterministic Systems
  • LLM Evaluation Metrics & Frameworks
  • Token Optimization
  • Cost Analysis & Latency Reduction
  • Automated Guardrails
  • Safety & Error Handling in Production AI
06Capstone Project

Capstone Project

30 Hours
  • Capstone Project is based on learning and students create prototype level solutions for real life problems.
  • Design Thinking – Way to solve problems with creative thinking.
07AI Engineering & Testing

Employability Skills

20 Hours
  • Mastering Self-Introduction & Personal Branding
  • Professional Communication
  • Smart Thinking
  • Career Readiness
  • Practice Session

Note – Final assessment and Capstone Project will be criteria for Course completion certificate.

IV Year — Value-Added Course

The Value-Added Course is a meticulously designed 135-hour intensive program tailored for B.Tech final year students. It aims to bridge the gap between academic knowledge and industry expectations by equipping students with specialized skills in enterprise systems, cloud platforms, edge computing, and professional readiness required for successful placements and industry roles. This program emphasizes hands-on learning, self-paced enterprise technology modules, project-based application, and real-world problem solving. Through structured modules covering SAP Developer Skills, SAP Business Cloud, Edge Computing & Embedded AI Systems, Cloud Computing Fundamentals, intensive Placement Practice, a comprehensive Capstone Project guided by Design Thinking principles, and dedicated Employability Skills, students will develop job-ready competencies that significantly enhance their technical confidence and employability in enterprise and emerging technology domains. The course integrates industry-relevant platforms such as SAP, major cloud providers (AWS/ Azure/ SAP/ BTP), embedded AI systems, and professional development practices, ensuring final-year students are well-prepared for internships, campus placements, and immediate industry contribution

Course Objectives

The primary objectives of this program are:

  • Develop foundational and practical expertise in SAP Developer Skills including ABAP, Fiori/UI5, S/4HANA, and SAP Cloud Platform development.
  • Build proficiency in SAP Business Cloud for data modeling, analytics, dashboards, predictive forecasting, and planning solutions.
  • Gain hands-on knowledge of Edge Computing and Embedded AI Systems, including IoT concepts, sensor integration, and deployment on devices such as Pico/PicoW.
  • Establish strong fundamentals in Cloud Computing covering architecture models, major platforms (AWS/Azure/SAP BTP), serverless concepts, security, and cost optimization.
  • Strengthen placement readiness through structured practice in aptitude, technical skills, coding, and domain knowledge.
  • Execute a substantial Capstone Project using Design Thinking methodology to deliver industry-relevant solutions.
  • Enhance professional communication, resume building, interview skills, and overall employability.
Program Benefits

The program offers numerous benefits that will significantly benefit B.Tech final year students:

  • Industry-Aligned Skill Development: Gain practical, job-relevant skills in high-demand enterprise technologies such as SAP, Cloud platforms, Edge Computing, and Embedded AI.
  • Enterprise Technology Exposure: Learn self-paced modules on SAP Developer Skills and SAP Business Cloud that are widely used across large organizations.
  • Strong Project Portfolio: Build multiple Mini Projects + 1 Major Capstone Project that can be showcased during placements and interviews.
  • Enhanced Placement Readiness: Dedicated Placement Practice and Employability Skills modules covering aptitude, coding, resume building, group discussions, and interview preparation.
  • Holistic Professional Development: Balanced mix of technical depth, cloud and edge technologies, Design Thinking, and soft skills under structured guidance.
  • Immediate Industry Applicability: Skills directly aligned with roles in enterprise IT, cloud engineering, embedded systems, and analytics.
Skill Set

Upon successful completion of the program, students will have developed the following advanced skill sets:

  • SAP Developer Skills: Understanding of SAP Basics & Architecture, ABAP Programming, SAP Fiori & UI5, S/4HANA Development, Business Workflow, debugging, performance optimization, and SAP Cloud Platform development.
  • SAP Business Cloud Expertise: Data modeling, dashboard & story creation, augmented analytics, predictive forecasting, planning & budgeting features, and data security & access control.
  • Edge Computing & Embedded AI Skills: IoT and Edge AI concepts, sensor integration, communication protocols, AI model optimization for edge devices, and hands-on programming with Pico/PicoW embedded systems.
  • Cloud Computing Fundamentals: Cloud architecture (IaaS, PaaS, SaaS), platform essentials of AWS, Azure and SAP BTP, cloud-native and serverless concepts, storage, networking, IAM, cost optimization, security, and monitoring.
  • Placement & Technical Readiness: Aptitude test preparation, technical skill reinforcement, coding practice, and domain-specific knowledge relevant to campus recruitment.
  • Design Thinking & Capstone Execution: Application of Empathize–Define–Ideate–Prototype–Test stages, user research, prototyping, usability testing, iteration, and delivery of real-world solutions.
  • Employability & Professional Skills: Self-awareness and goal setting, resume writing & LinkedIn profiling, email etiquette, presentation skills, group discussion, interview skills, interpersonal skills, and structured problem-solving approaches.
Prerequisites
  • Basic knowledge of using internet and computer systems.
  • Basic understanding of problem solving.
  • Introductory understanding of programming.
  • Elementary concepts of Probability theory, Statistics & Continuous variable Calculus.

Program Outline

Module No Module Name Estimated Hours
1 SAP Developer Skills 11
2 SAP Business Cloud 11
3 Edge Computing & Embedded AI Systems 11
4 Cloud Computing Fundamentals 14
5 Placement Practice 13
6 Capstone Project 60
7 Employability Skills Course 15
Total Duration 135
01SAP Developer Skills

SAP Developer Skills

11 Hours
  • SAP Basics & Architecture
  • SAP ABAP Programming
  • SAP Fiori & UI5
  • SAP S/4HANA Development
  • SAP Business Workflow
  • Debugging & Performance Optimization
  • SAP Cloud Platform Development
02SAP Business Cloud

SAP Business Cloud

11 Hours
  • Introduction to SAP Business Cloud
  • Data Modeling & Connectivity
  • Creating Dashboards & Stories
  • Augmented Analytics & Predictive Forecasting
  • Planning & Budgeting Features
  • Data Security & Access Control
03Edge Computing & Embedded AI Systems

Edge Computing & Embedded AI Systems

11 Hours
  • Introduction to Edge Computing
  • IoT & Edge AI Concepts
  • Edge Device Communication Protocols and Sensor Integration
  • AI Model Optimization for Edge Devices
  • Pico/PicoW Embedded Device Programming & Deployment
04Cloud Computing Fundamentals

Cloud Computing Fundamentals

14 Hours
  • Cloud Computing Architecture
  • Platform Deep Dive: AWS / Azure / SAP BTP Essentials
  • Cloud-Native Deployment & Serverless Concepts
  • Cloud Storage
  • Networking & Identity Management
  • Cost Optimization
  • Security & Monitoring in Cloud Environments
05Placement Practice

Placement Practice

13 Hours
  • Aptitude Tests
  • Technical Skills
  • Coding Practice
  • Domain Specific Knowledge
06Capstone Project

Capstone Project

60 Hours
  • Capstone Project is based on learning, and students create prototype level solutions for real life problems.
  • Design Thinking - Way to solve problems with creative thinking.
05Placement Practice

Employability Skills Course

15 Hours
  • Introduction & Orientation, Self-awareness & Goal setting
  • Resume Writing & LinkedIn profiling, E-mail etiquette & Employability Tests
  • Presentation Skills , Group Discussion , Interview Skills
  • Interpersonal Skills and Approach to Problem-solving

Faculty Development Program on Machine Learning, Computer Vision, Prompt Engineering, Agentic AI, & SAP Tech Skills

5 Days Workshop

Workshop Objective / Outcome

  • Bridge the Industry-Academia Gap: Expose faculty and educators to the latest technology trends in Generative AI, Agentic Workflows, Computer Vision, and SAP enterprise solutions.
  • Experiential Learning: Hands-on experience in building intelligent systems, leveraging prompt engineering strategies, creating no-code AI agents, and analysing data.
  • Generative & Agentic AI Proficiency: Master prompt design principles and construct custom no-code AI agents for academic and real-world applications.
  • Computer Vision & ML Applications: Learn how to utilize Python libraries (NumPy, Pandas, Matplotlib, OpenCV, Keras) to construct, evaluate, and deploy Machine Learning and Deep Learning models.
  • Enterprise SAP Tech Skills: Develop core SAP technical competencies to build and deploy applications using SAP BTP ABAP Environment and SAP Analytics Cloud.
  • Data-Driven Decision Making: Equip participants to seamlessly translate data-driven insights and AI capabilities into actionable academic and corporate decisions.

Workshop Prerequisite

  • Prior basic knowledge of Python programming and foundational packages (e.g., NumPy, Pandas, scikit-learn) is an advantage.
  • Working familiarity with Python IDEs (such as Jupyter Notebook or Anaconda).
  • Familiarity with web browsers and web-based AI platforms/interfaces.
  • Basic understanding of Linux commands or terminal environments is beneficial but not mandatory.

Tools / Software Requirements

  • Hardware: Laptop or desktop computer with a minimum Intel i3 processor (or equivalent), 4GB+ RAM, running 64-bit Windows 10/11 or macOS, with stable internet connectivity. 
  • Software/Environment:
    • Anaconda Distribution with Python 3.x installed.
    • Modern web browser (Chrome, Edge, or Firefox) to access no-code Agentic AI platforms.
    • Network Requirement: Uninterrupted high-speed Wi-Fi or LAN connection without restrictive corporate proxy settings for seamless API and platform access during training.

Workshop Agenda

Sl. No. Topic Description Duration (Hrs.) (30 Hrs.)
FDP Pre-Assessment (Baseline)
Day-1
1. Introduction – Workshop Agenda, Objective Data Analytics with Python
  • Numerical Python - NumPy
  • Pandas (Data Manipulation and data analysis)
  • Data Visualization Using Matplotlib and Seaborn
Machine Learning Algorithms
  • Introduction – Machine Learning – Supervised, unsupervised ML
  • Linear Machine learning model (linear regression / logistic regression and it’s Evaluation matrices).
 6 Hrs
Day-2
2. Machine Learning Algorithms
  • Ensemble Machine learning models
  • Dimensionality Reduction Techniques (PCA)
  • Non-Linear Model (SVM & KNN)
 6 Hrs
3. Deep Learning
  • Neural Networks – Neurons, Loss Functions, Weights
  • Gradient Descent and Back propagation
  • Convolutional Neural Network
  • Computer Vision – With OpenCV and Keras
Day-3
4. Hands-on session on Computer Vision
  • Canny edge detection
  • Viola-Jones Algorithm for face detection
  • Face detection
  • Full body detection
  • Number plate detection
 6 Hrs
5. Prompt Engineering
  • Fundamentals of Prompting & LLM Architecture Overview
  • Key Techniques: Zero-shot, Few-shot, Chain-of-Thought (CoT) Prompting
  • System Prompts, Role Prompting, and Parameter Tuning (Temperature, Top-P)
  • Designing Robust Prompts for Academic Research, Summarization, and Code Generation
Day-4
6. Agentic AI Basics – No-Code Platform
  • Introduction to Agentic AI Systems & Multi-Agent Frameworks
  • Understanding AI Agents vs. Standard Chatbots
  • Hands-on Exploration of No-Code Agentic Platforms
  • Building Custom AI Workflows, Web-Search Agents, and Document (RAG) Assistants
SAP Tech Skills SAP Business Technology Platform ABAP Environment
  • Understanding the SAP BTP platform
  • Creating a BTP ABAP Environment
  • Creating an ABAP Package
 6 Hrs
Day-5
7. ABAP RESTful Programming Model
  • Condition statement and looping with ABAP
  • Creating a Database Table
  • Create an ABAP Class
  • Conditional statement and looping with SAP ABAP
  • Introduction of ABAP RESTful Application Programming Model
  • Developing a Read-Only List Report App
  • Enabling the Transactional Behavior of an App
  • Dealing with Existing Code
SAP Analytics Cloud
  • Getting Started with Stories
  • Building Stories
  • Configuring Story Elements
  • Manipulating Data in Stories
  • Presenting Stories
 6 Hrs
FDP Post-assessment and Feedback
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