Introduction to Artificial Intelligence
and Machine Learning
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
4.5 Level NCrF
National Credit Framework
30 Hours
Flexible Learning
Beginner Level
No prior experience required
Nano Credit Course
01 Credit
Course Overview
This course offers a beginner-friendly introduction to Artificial Intelligence (AI) and Machine Learning (ML) through hands-on learning. Students work with real datasets, gaining a visual understanding of supervised and unsupervised models using low-code environments. By the end of the course, learners will have the skills to explore practical AI/ML applications and build simple models in the cloud.
Key Learning Highlights
-
Practical, hands-on sessions using Google Colab
Exposure to cloud-based ML tools
Low-code AI/ML implementation for easy understanding
Visual introduction to supervised & unsupervised learning
Work with real-world datasets
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
- Understand foundational concepts in AI and ML.
- Navigate Google Colab and cloud-based AI tools effectively.
- Apply basic supervised and unsupervised algorithms.
- Analyse datasets and generate visual insights.
- Build and evaluate simple ML models using cloud platforms.
Master the course with just 5 Modules
This is an exploration of Artificial Intelligence and Machine Learning from a practical and conceptual perspective. The journey begins with the foundational ideas and algorithms that shaped AI/ML, as traditional approaches to problem-solving give way to data-driven methods. What follows is an era of rapid innovation and experimentation, as researchers, developers, and industries compete to define the future of intelligent systems. The course concludes with present-day applications and trends, as communities and organisations worldwide transition into an AI-powered era.
Introduction to AI and ML
- Definition and evolution of AI and ML
- Applications of AI/ML in industry
- Types of learning: supervised, unsupervised, reinforcement
Getting Started with Google Colab
Overview of cloud-based notebooks
Basic Python scripting
Using markdown and code cells
Importing datasets
Data Handling and Visualization
- Using Pandas and Matplotlib to read, clean, and visualise data
- Descriptive statistics
Building a Basic ML Model
Linear regression
Classification with decision trees using scikit-learn
Model training, testing, and evaluation
Capstone Mini Project
End-to-end model on a real dataset (e.g., predicting student grades or housing prices)
Report preparation and presentation
Roles
- AI/ML Lab Assistant
