Nano & Micro Credit Courses in AI & ML
Level Up with AI: Bite-Sized Courses, Big Impact
At MIET, we offer Nano (0.5–1 credit) and Micro (2–4 credits) courses designed to deliver modular, industry-relevant skills in cybersecurity. These courses are aligned with emerging roles in security operations, ethical hacking, digital forensics, and cloud security, providing flexible and stackable learning pathways for students and professionals alike.
Whether you’re starting your journey with basic cyber hygiene or diving into advanced topics like threat intelligence and red teaming, our credit courses are:
Nano Credit Courses in AI & ML
Introduction to AI & ML using Google Colab
Hands-on learning through Google Colab with real datasets; intro to supervised and unsupervised models using low-code tools.
- 🕒 30 Hours
- 🎓 01 Credit
Prompt Engineering for Generative AI
Tool-based exposure to ChatGPT / DALL·E; real-time prompt tuning; applications in content creation, search optimisation, and task automation
- 🕒 15 Hours
- 🎓 0.5 Credit
Model Evaluation and Tuning Basics
Skill-building on performance metrics like confusion matrix, precision-recall, and ROC; uses real model outputs for diagnostic practice
- 🕒 15 Hours
- 🎓 0.5 Credit
Python for AI: Basics with NumPy & Pandas
Coding-first approach for AI foundations; focuses on structured data handling, preprocessing, and visualisation with industry-standard libraries
- 🕒 30 Hours
- 🎓 01 Credit
AI in Excel using Microsoft Copilot
No-code AI applications using Microsoft 365 Copilot; practical use in forecasting, summarizing, and automating business insights.
- 🕒 15 Hours
- 🎓 0.5 Credit
Micro Credit Courses in AI & ML
Computer Vision with TensorFlow and OpenCV
Full stack skill training in CV pipelines; covers image classification, object detection; real-time data with TensorFlow and OpenCV
- 🕒 90 Hours
- 🎓 03 Credit
Natural Language Processing using Hugging Face
Skill-based training in fine-tuning LLMs like BERT; hands-on use of Hugging Face Transformers for text classification, sentiment analysis
- 🕒 90 Hours
- 🎓 03 Credit
Applied Machine Learning using Python and scikit-learn
End-to-end ML pipeline development on real datasets; feature engineering, model training, evaluation; project-based learning
- 🕒 60 Hours
- 🎓 02 Credit
AI Model Deployment with Docker and FastAPI
Real-world skillset in containerizing and deploying ML models as REST APIs; combines DevOps with ML workflows
- 🕒 60 Hours
- 🎓 02 Credit
AI Model Deployment with Docker and FastAPI
Real-world skillset in containerizing and deploying ML models as REST APIs; combines DevOps with ML workflows
- 🕒 60 Hours
- 🎓 02 Credit
