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.
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
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
Model training and testing basics
Model performance evaluation
Mini supervised learning project implementation
