Basics of Supervised and Unsupervised Learning

Yes. This is a free online self-paced course designed for beginners.

You will learn the basics of supervised and unsupervised learning, including regression, classification, clustering, and their applications.

No. The course focuses on concepts and does not require prior coding experience.

Anyone interested in machine learning, including students, beginners, and professionals, can join.

Yes. Learners receive an e-Certification after completing the course.

Supervised learning is a type of machine learning where models learn from labeled data to make predictions or classify new information.

Unsupervised learning is a type of machine learning where models work with unlabeled data to discover patterns, groups, or hidden structures.

Yes. The course introduces regression and classification as important supervised learning techniques.

Yes. The course introduces clustering as a basic unsupervised learning method used to discover groups and patterns in data.

Yes. This course builds a clear foundation in supervised and unsupervised learning, making it useful before moving into advanced machine learning, artificial intelligence, and data science topics. The Basics of Supervised and Unsupervised Learning course provides a clear and simple foundation in the two main types of machine learning. It helps learners understand how data is used to train models, discover patterns, and solve real-world problems, making it an ideal starting point for further learning in AI and data science.