Supervised vs. Unsupervised Machine Learning in Finance simply explained

Imagine you're teaching a computer to recognize different types of fruits. You show it pictures of apples, oranges, and bananas, and you tell the computer which fruit is in each picture. This is like giving the computer a "supervising" teacher who guides it through learning.

In supervised learning, you have a teacher (the labeled data) that helps the computer learn by showing it examples of inputs (like images) and their corresponding correct answers (like labels). The computer learns patterns from these examples and uses them to make predictions on new, unseen examples. It's like the computer is learning to match patterns and associate them with specific outcomes.

Unsupervised Machine Learning:

Now, let's say you give the computer a bunch of mixed-up pictures of fruits without telling it what each fruit is. You ask the computer to find any patterns or similarities on its own. The computer tries to group similar pictures together based on the features it detects.

In unsupervised learning, there's no teacher or labeled data. The computer explores the data on its own and tries to find interesting patterns or groupings.


To sum it up:


- Supervised Learning:

Learning with a teacher, using labeled examples to predict outcomes.

- Unsupervised Learning: Exploring data without a teacher, finding patterns or groupings on its own.


💡 Unsupervised Machine Learning in Finance:


Now, let's say you give the computer a bunch of data about various financial indicators without telling it whether the stocks went up or down. You ask the computer to find any hidden patterns or groupings in the data. The computer looks for similarities between the indicators and tries to organize them in a meaningful way.


In unsupervised learning for finance, there's no teacher giving specific answers. The computer explores the financial data on its own and tries to discover relationships between different indicators. It's like the computer is exploring the financial landscape to uncover valuable insights and connections that might not be obvious at first glance.


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