Supervised Learning vs Unsupervised Learning | Epic Battle of Data Science

In this Epic Battle of Data Science, we are discussing the concepts of Supervised Learning and Unsupervised Learning.
Supervised Learning 🔥
Introduction ✅
I am Supervised Learning! My expertise lies in working with labeled data, thanks to human guidance. My training process involves learning from historical data, enabling me to make predictions for future events.
Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well-labelled.
Types of Problems ✅
When it comes to tackling classification problems or regression tasks, I'm the go-to choice. My capabilities shine in these scenarios.
When to Use ✅
Whenever a company like Netflix seeks to predict its stock prices or a bank needs to determine loan eligibility, it's me they turn to for assistance.
Unsupervised Learning 🔥
Introduction ✅
I'm Unsupervised Learning, and I have a unique talent. I can handle data without the need for explicit human guidance or labeling. I thrive on self-discovery, uncovering hidden patterns, and grouping data organically.
In Unsupervised Learning, unlike supervised learning, no teacher is provided which means no training will be given to the machine. Therefore the machine is restricted to finding the hidden structure in unlabeled data by itself.
Types of Problems ✅
When the goal is to detect anomalies or cluster data in specific ways, that's where I come into play.
When to Use ✅
Think about scenarios where Netflix wants to recommend movies or identify audience segments. Even when a bank seeks to detect anomalies in transactions, I'm the one they call upon for the task.

[@SaiRam-jj1sw]

💥🔥🔥🔥🔥

[@swetasharma8467]

Well explained 👏 👏

[@elevateyour_essence484]

Unsupervised Wins

[@ayushrana5753]

Op