Supervised Learning — Teaching under assistance

Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. i.e., we have prior knowledge of what the output values for our samples should be.

Supervised learning is when we teach or train the machine using data that is well-labeled, which means some data is already tagged with the correct answer. The intent will predict the label for unseen or future data using labeled historical data.

Let’s talk Hakuna Matata way,

Supervised learning is like having a teacher guide you step by step, providing examples and corrections as you learn.

Learning to Ride a Bike: When you were first learning to ride a bike, you likely had someone, perhaps a parent or guardian, holding onto the back of the bike to provide support and guidance. They would give you instructions, and show you how to balance and correct your mistakes as you practiced.

This is similar to supervised learning, where the teacher (or supervisor) provides labeled examples (guidance) to help you learn.

In supervised learning, the algorithm learns from labeled examples provided by a supervisor or teacher, aiming to predict the correct output when given new input data.

The goal is for the model to generalize well to unseen data, similar to how you aim to apply what you’ve learned in real-world situations beyond the examples provided during learning.

In a Nutshell,

1. Spam Email Filter: Supervised learning algorithms can be used to train spam filters to identify and block spam emails.

Key Takeaways:

•  Supervised Learning — Algorithms learn from labeled data.
• All the classification and regression algorithms are examples of supervised learning.

Unsupervised Learning – Learning patterns on your own ( Teaching with out assistance )

See the Hakuna Matata meaning:-

Imagine you’re going on a hike in a forest you’ve never been to before. You start exploring the trails, observing different kinds of trees, flowers, and animals along the way. You don’t have a guide telling you the names of each plant or creature.

Instead, you’re just taking in all the information and noticing patterns on your own.

As you walk, you start to see similarities between certain plants or animals. Maybe you notice that there are several trees with similar leaves grouped, or you see different birds flying in the same area.

Through these observations, your brain starts to categorize things without anyone explicitly telling you what category each thing belongs to.

  Key Takeaways:

• In Unsupervised learning, algorithms analyse the data without being told what the “correct” output should be Instead, they look for patterns, similarities, or differences within the data to organize and make sense of it.

Bonus Tip:

Classification Vs. Clustering – Difference

Happy Learning !! ☺️