What are the key differences between supervised and unsupervised learning?
In data science, understanding the distinction between supervised and unsupervised learning is crucial for selecting the right algorithm for your data analysis. Supervised learning involves training a model on a labeled dataset, where the outcome variable, also known as the target, is already known. This allows the model to learn by example. On the other hand, unsupervised learning deals with unlabeled data, and the goal is to identify underlying patterns or groupings without any predefined labels. Both approaches are fundamental to machine learning and have unique applications and challenges.
-
Monis Siddiqui| 44k+on Linkedin |AI& ML engineering | Google AP Virtual Intern ||Software Test Automation Virtual Internship|…
-
Durga Satya Sai Kiran ChitturiAspiring Data Scientist | ASE @Tech Mahindra
-
Pallavi SatsangiAI-ML Manager| Lead Data Scientist |Expert in AI delivery| MSc.Business Analytics| Generative AI,Machine Learning, Data…