Master’s in Applied Artificial Intelligence

Learn to build real-world AI solutions: ML, Deep Learning, and GenAI—responsible, practical, and business-driven.

Overview

A hands-on program to build end-to-end AI systems: data preparation, training, evaluation, deployment, and governance. Includes MLOps, ethics, privacy, and real business applications.

Studies Plan (Courses / Modules)

• Prepare data and train supervised/unsupervised models.

• Evaluate models with proper metrics and validation.

• Apply Deep Learning and modern techniques.

• Build responsible GenAI/LLM applications.

• Deploy and monitor models with MLOps.

Who it’s for

• Professionals applying AI in their industry.

• Developers/data engineers moving into ML/MLOps.

• Analysts who want to build intelligent models and products.

What you’ll be able to do

• M1 — Foundations: Python, Data & AI Mindset

  Python for data, exploration, cleaning, and AI fundamentals.

  Topics: notebooks · EDA · data pipelines

• M2 — Machine Learning Fundamentals

  Classic models, feature engineering, validation, and metrics.

  Topics: regression/classification · overfitting · cross-validation

• M3 — Data Engineering for AI

  Data preparation at scale, quality, and traceability.

  Topics: ETL · data versioning · feature stores (intro)

• M4 — Deep Learning & Neural Networks

  Neural networks and efficient training.

  Topics: CNN/RNN (depending on focus) · regularization · optimization

• M5 — Natural Language Processing (NLP)

  Text processing and modern NLP methods.

  Topics: embeddings · transformers (intro) · NLP evaluation

• M6 — Generative AI & LLM Applications

  Build LLM apps: prompting, RAG, tools, and guardrails.

  Topics: RAG · evaluation · safety & hallucinations

• M7 — Responsible AI: Ethics, Privacy & Governance

  Bias, explainability, compliance, and responsible design.

  Topics: fairness · privacy · auditing & documentation

• M8 — MLOps: Deployment, Monitoring & Lifecycle

  ML lifecycle: deployment, monitoring, drift, retraining.

  Topics: ML CI/CD · model registry · metrics

• M9 — Capstone Project

  A real-world AI solution with delivery and demo.

  Deliverables: dataset/experiments · model · deployment · report

• Available seats : 25