Difference Between Supervised and Unsupervised Machine Learning
Machine learning (ML) has become a buzzword in today’s tech-driven world, and for good reason. It's the driving force behind many of the applications we use daily, from recommendation systems to autonomous vehicles. However, machine learning isn’t just a single monolithic concept. It branches out into various types, primarily supervised and unsupervised learning. Let's dive deep into these two major paradigms, exploring their differences, applications, algorithms, pros and cons, and much more. Buckle up, and let’s embark on this ML adventure! Supervised Learning: The Teacher’s Pet Supervised learning is akin to a student learning from a teacher. In this scenario, the teacher (a labeled dataset) provides the correct answers, and the student (the machine learning algorithm) learns by comparing its predictions with these correct answers. Over time, the algorithm tweaks its predictions to get closer and closer to the actual answers. How It Works Labeled Data: The key ingredient h