Course Overview

Enterprise AI: Designing, Deploying, and Scaling AI Solutions for Business Transformation

Lead Instructor: Professor Dr. Emdad Khan (CEO, InternetSpeech Inc. USA)

Co-Instructor: Md. Ridoy Sarkar (Lecturer, Daffodil International University)

⏱ Duration: 10 Weeks | 48 Hours (Total)

Course Description

This course is designed to bridge the critical gap between technical AI skills and business value creation. Moving beyond experimental models, this program equips professionals to architect, govern, and scale AI solutions across large organizations. It focuses on integrating AI with legacy systems (ERP, CRM), ensuring compliance, and optimizing ROI for sustainable business transformation.

Evidence of Demand

With the rapid rise of AI and Digital Transformation, organizations are moving beyond experimental AI models to scalable, governed enterprise solutions. There is a critical gap for professionals who can bridge technical AI skills (Data Science, LLMs) with business value creation (ROI, Compliance, Integration). Industry demands architects who can deploy AI across supply chains, HR, and finance at scale.

Purpose and Objectives

The purpose of this course is to equip students with the skills to architect, govern, and scale AI solutions in large organizations. Objectives:

• Understand Enterprise AI architecture & Digital Twins.
• Learn to integrate AI with legacy systems (ERP, CRM).
• Master AI Governance, Security, and Compliance.
• Develop strategies for ROI optimization and scaling.

Course Content & Class Plan (Modules)

Tentative Syllabus:

• Module 1: Intro to Enterprise AI (Context & Maturity Models)
• Module 2: Generative AI & LLMs (RAG, Knowledge Assistants)
• Module 3: Enterprise Data Foundations (Data Lakes, Governance)
• Module 4: AI Solution Architecture (Cloud vs. On-Prem, Microservices)
• Module 5: Enterprise AI Use Cases & Case Studies (Finance, Health, Mfg)
• Module 6: AI & Agentic Workflow Development (Lifecycle, Feature Eng.)
• Module 7: MLOps & Model Lifecycle Management (CI/CD, Monitoring)
• Module 8: AI Integration with Enterprise Systems (ERP/CRM APIs)
• Module 9: AI Governance, Security & Compliance (Privacy, Ethics, Transparency)
• Module 10: Decision Intelligence & Analytics (Predictive vs. Prescriptive)
• Module 11: Scaling & Performance Optimization (Cost/Reliability)
• Module 12: Capstone Project (Design & Implementation)

Practical & Field Work

Note: Practical assessments constitute approximately 70% of the course grade.

• Labs & System Design: Hands-on tasks involving MLOps tools (Docker, MLflow), visualization (PowerBI/Tableau), and Python environments.
• Capstone Project: Students will design and implement a complete Enterprise AI solution, including defining the business problem, creating the architecture, and providing a live demonstration.
• Examples include: AI-Powered Academic Administration Systems, Enterprise Knowledge Assistants (RAG-based), and Predictive Analytics Dashboards.

Learning Outcomes

Upon completion, students will be able to:

• Strategize: Identify AI opportunities for business optimization.
• Architect: Design scalable Cloud/Hybrid AI systems.
• Integrate: Connect AI with enterprise apps via APIs.
• Govern: Implement security and compliance frameworks.
• Evaluate: Measure AI success via ROI and impact.
• Scale: Deploy sustainable systems using MLOps.

Target Audience

• CSE/SWE/CIS/ITM Students (3rd/4th Year)
• Data Scientists & ML Engineers
• Solution Architects & Project Managers
• Industry Professionals seeking AI leadership roles

Entry Requirements

• Minimum Age: 20 Years
• Basic Programming: Python recommended
• Foundational Knowledge: Introduction to AI/Machine Learning and fundamentals of Databases & Information Systems
• Resource Requirements: Internet/Cloud Access

Career Pathways

• Enterprise AI Architect
• AI Product Manager
• MLOps Engineer
• AI Transformation & Digital Transformation Consultant
• Chief AI Officer (CAIO) track

Tools & Resources

• Programming: Python Environment (Jupyter/Colab)
• MLOps Tools: Docker, Kubernetes, MLflow, Jenkins/GitHub Actions
• Cloud Platforms: AWS / Azure / Google Cloud (Conceptual & Hands-on)
• ML Frameworks: Scikit-learn, TensorFlow, PyTorch
• Visualization: PowerBI / Tableau

Assessment Criteria

• Case studies & assignments: 30%
• Labs & system design tasks: 30%
• Final project: 40%

Standard Course Fee: Tk 15,000/-

The following discounts currently apply:

• Daffodil Students: Tk 5,000/- (67% Discount Promotional Offer)
• Non-Daffodil Students: Tk 7,500/- 

Course Overview

Medical Image Processing with Machine Learning : Revolutionize Healthcare Diagnostics with AI & Computer Vision

Instructors:

• Dr. S M Hasan Mahmud (Associate Professor, Dept. of SWE, DIU)
• Md. Tarek Aziz (AI Engineer, ATI Limited)

⏱ Duration: 6 Weeks | 24 Contact Hours (Blended: Online Lectures + In-person/Virtual Labs)

Evidence of Demand

The global AI in Healthcare market is currently experiencing explosive growth, creating a massive skills gap in the intersection of medicine and technology. Hospitals, diagnostic centers, and HealthTech startups are actively seeking specialized engineers who can analyze complex medical data—such as MRI, CT scans, and X-Rays—using Artificial Intelligence.

This skill set is becoming critical for the modern telemedicine and automated diagnostics industry, particularly for tasks such as the automated detection of tumors, pneumonia, and other pathologies. Despite the high demand, there is a scarcity of professionals capable of bridging the technical gap between traditional biomedical engineering and modern machine learning implementation.

Purpose and Objectives

This course is designed to bridge the gap between Biomedical Engineering and Artificial Intelligence, equipping learners with the ability to build automated diagnostic systems.

The course aims to:

• Master Pixel-Level Analysis: Provide deep technical knowledge of medical image formats (DICOM/NIfTI) and manipulation.
• Apply Advanced AI: Teach the application of Machine Learning and Deep Learning to solve real-world diagnostic problems.
• Automate Diagnostics: Enable students to build systems for detecting diseases like Cancer or Pneumonia automatically.
• Bridge Theory and Practice: Connect academic theory with real-world medical datasets to ensure industry readiness.

Detailed Course Content & Class Plan

This course follows a structured week-by-week progression from foundational image handling to advanced Deep Learning deployment.

Week 1: Introduction & Image Handling

• Theory: Understanding medical image formats (DICOM vs. NIfTI) and Ethics in Medical AI.
• Tools: OpenCV, NumPy, pydicom, Matplotlib.
• Lab: Loading, visualizing, and manipulating real MRI/CT scans.

Week 2: Preprocessing Techniques

• Theory: The critical importance of data cleaning in Medical AI pipelines.
• Techniques: Noise reduction, Histogram equalization, and Image normalization.
• Lab: Enhancing low-quality MRI data to improve analysis accuracy.

Week 3: Image Segmentation

• Theory: Strategies to separate the "Disease" (ROI) from the "Background" anatomy.
• Techniques: Thresholding, Region-growing, and Watershed algorithms.
• Lab: Automated Tumor Segmentation task.

Week 4: Feature Extraction & ML Classifiers

• Theory: Converting images into quantitative data using Statistical & Texture features.
• Techniques: Haralick features and Shape descriptors.
• Lab: Training classic ML classifiers (SVM/Random Forest) to detect Pneumonia patterns and evaluating with ROC/Confusion Matrices.

Week 5: Deep Learning in Medical Imaging

• Theory: Leveraging the power of Convolutional Neural Networks (CNNs) in diagnostics.
• Techniques: Transfer Learning (VGG16, ResNet) and Explainable AI (Grad-CAM) to visualize model decisions.
• Lab: Building a Deep Learning model for Multi-class Disease Classification.

Week 6: Capstone Project & Showcase

• Project: Design and build an end-to-end diagnostic solution (e.g., "Brain Tumor Detection System" or "Bone Fracture Analyzer").
• Presentation: Defense of findings, model accuracy, and real-world applicability to industry experts.

Learning Outcomes

After completing the course, participants will be able to:

• Process Medical Data: Handle and manipulate complex medical image formats using Python libraries.
• Enhance Quality: Apply noise reduction and preprocessing techniques to improve diagnostic clarity.
• Segment Anatomy: Isolate critical regions, such as tumors, from surrounding tissue.
• Train Models: Develop and train Machine Learning and Deep Learning models for precise disease classification.
• Deploy Pipelines: create a complete, end-to-end medical imaging analysis pipeline.

Target Audience

• University Students: 3rd to 4th-year students in CSE, BME, EEE, CIS, or SWE.
• Medical Professionals: Radiologists and clinicians interested in the intersection of healthcare and AI.
• Data Scientists: Professionals looking to pivot their career into the Healthcare domain.
• ML Engineers: Practitioners seeking specialized skills in computer vision for medicine.

Entry Qualifications

• Programming: Basic Python programming skills are required.
• Interest: A strong interest in Medical AI (Basic ML knowledge is helpful but not mandatory).
• Minimum Age: 18 Years.

Progression Pathway

After completing this micro-credential, learners may progress to roles such as:

• Medical Computer Vision Engineer
• Biomedical Data Scientist
• AI Research Assistant
• HealthTech Entrepreneur

Assessment Criteria

• Final Capstone Project: 40% (End-to-end solution design)
• Mini Projects (Labs): 30% (Practical implementation of weekly concepts)
• Weekly Quizzes: 20% (Theoretical understanding)
• Participation: 10% (Engagement in lectures and labs)

Resource Requirements

Tools, Instruments & Materials

• Software Environment: Python (Jupyter Notebook / Google Colab).
• Libraries: OpenCV, NumPy, pydicom, Matplotlib, TensorFlow/Keras or PyTorch.
• Hardware: Computers with GPU support (or Cloud access).
• Data: Real-world Medical Datasets (e.g., Kaggle, NIH X-Ray).

Standard Course Fee: Tk 5,000/-

The following discounts currently apply:

• Daffodil Students: Tk 3,000/-
• Non-Daffodil Students: Tk 3,000/- (Discounted from BDT 5,000)