What is Machine Learning?

Machine Learning

Machine learning is where computers learn from data and improve over time without needing external instructions. These are different type of machine learning: 

1. Supervised Learning

Common boring theory:
Supervised learning is a type of machine learning where the algorithm learns from labeled data. It uses these labels to find relationships between inputs and outputs, helping it make predictions on new, unseen data.

In human words:
It’s like teaching a dog tricks using treats. You show the dog exactly how to sit or roll over, and each time it gets the right answer, it gets rewarded. Over time, the dog learns what to do to get more treats. That’s how supervised learning works—it learns by example, where the right answer is already given.

2. Unsupervised Learning

Common boring theory:
Unsupervised learning involves training a model on data without any labels. The goal is to find patterns or groupings in the data without any predefined categories.

In human words:
Imagine a group of people at a party. Without anyone telling them, they naturally start clustering into smaller groups based on shared interests, like people who like the same type of music. Unsupervised learning is like the computer finding these hidden groups in data on its own.

3. Reinforcement Learning

Common boring theory:
Reinforcement learning is where an agent learns by interacting with its environment. It makes decisions, receives feedback (rewards or penalties), and adjusts its actions accordingly to maximize rewards over time.

In human words:
Think of training a pet with treats. Every time it performs a trick correctly, it gets a treat (reward). If it messes up, no treat (penalty). Over time, the pet learns which actions lead to treats and which don’t. That’s how reinforcement learning teaches the system to make better decisions.

Congratulations, you learned something today! Cheers!