Introduction to Machine Learning

Start your journey into Artificial Intelligence by learning the core concepts of Machine Learning with a strong focus on Supervised Learning techniques used in real-world industry applications.
This course is designed to help beginners and working professionals understand how machine learning models are built, trained, and evaluated. You will learn key ML terminology, regression models, classification techniques, model evaluation strategies, and the k-Nearest Neighbors (k-NN) algorithm through structured micro-learning sessions.
Along with technical concepts, the course also focuses on how to communicate model performance to non-technical stakeholders, which is a critical real-world skill.
By the end of the course, you will be able to understand supervised learning workflows, evaluate model performance correctly, and build basic ML models with confidence.

🎯 What You Will Learn


Fundamentals of Machine Learning and AI terminology


Supervised Learning concepts and workflows


Linear and Polynomial Regression models


Regularization techniques to avoid overfitting


Classification using Logistic Regression


Model evaluation metrics and cross-validation


k-Nearest Neighbors (k-NN) algorithm fundamentals


Practical supervised learning mini project


How to explain ML results to business teams





👨‍💻 Who This Course Is For


Engineering Students (CSE / IT / ECE / AI / Data Science)


Telecom & Software Professionals entering AI/ML domain


Beginners starting Machine Learning


Data Analysts upgrading to ML roles


Professionals preparing for AI/ML interviews





🧪 Practical Exposure


Model training and testing basics


Model performance evaluation


Mini supervised learning project implementation



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