Mr. Goldstein: Created page with "==== 10.2.1 Supervised Learning (Regression, Classification) ==== Imagine you're learning to identify different types of fruits. Someone shows you many pictures, and for each picture, they tell you: "This is an apple," "This is a banana," "This is an orange." After seeing many examples with the correct labels, you learn to identify new fruits on your own. '''Supervised Learning''' is the most common type of Machine Learning. It's like having a teacher (the "supervisor")..."

2025-07-08T15:12:13Z

Created page with "==== 10.2.1 Supervised Learning (Regression, Classification) ==== Imagine you're learning to identify different types of fruits. Someone shows you many pictures, and for each picture, they tell you: "This is an apple," "This is a banana," "This is an orange." After seeing many examples with the correct labels, you learn to identify new fruits on your own. '''Supervised Learning''' is the most common type of Machine Learning. It's like having a teacher (the "supervisor")..."

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==== 10.2.1 Supervised Learning (Regression, Classification) ====
Imagine you're learning to identify different types of fruits. Someone shows you many pictures, and for each picture, they tell you: "This is an apple," "This is a banana," "This is an orange." After seeing many examples with the correct labels, you learn to identify new fruits on your own.

'''Supervised Learning''' is the most common type of Machine Learning. It's like having a teacher (the "supervisor") who provides the computer with '''labeled data'''. This means the computer gets lots of examples where both the input (like a picture of a fruit) and the correct output (like "apple") are known. The computer learns to find patterns that connect the inputs to the correct outputs.

Once the computer has learned from enough labeled examples, it can then make predictions or decisions about ''new, unseen data''.

There are two main types of problems that supervised learning solves:

* '''Regression:'''
** '''What it does:''' Predicts a '''continuous number''' (a value that can be any number within a range).
** '''Think of it like:''' Predicting a specific number.
** '''Examples:'''
*** Predicting the '''price of a house''' based on its size, number of bedrooms, and location. (The price can be any number like $300,000.50, $450,123.75, etc.)
*** Predicting tomorrow's '''temperature''' in degrees Celsius or Fahrenheit.
*** Estimating a person's '''age''' based on their photo.
* '''Classification:'''
** '''What it does:''' Predicts a '''category or class''' (a specific label from a set of choices).
** '''Think of it like:''' Sorting things into different bins.
** '''Examples:'''
*** Determining if an email is '''"spam" or "not spam."''' (Two categories)
*** Identifying if a picture contains a '''"cat," "dog," or "bird."''' (Multiple categories)
*** Deciding if a customer will '''"buy" or "not buy"''' a product.
*** Classifying a handwritten digit as <code>0</code>, <code>1</code>, <code>2</code>, ..., <code>9</code>.

Supervised learning is powerful because it allows computers to learn from past experiences and apply that knowledge to new situations where we need a specific answer or category.

'''Bibliography:'''

* '''IBM - What is supervised learning?''': https://www.ibm.com/cloud/learn/supervised-learning
* '''GeeksforGeeks - Supervised Machine Learning''': https://www.geeksforgeeks.org/supervised-machine-learning/
* '''Wikipedia - Supervised learning''': https://en.wikipedia.org/wiki/Supervised_learning

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