Remember Trigonometry? Thats how you use it in ML

📰 Medium · Python

Learn how trigonometry is used in machine learning to build a movie recommendation system using vectors and cosine similarity

intermediate Published 25 Apr 2026

Action Steps

Visualize movies as vectors in a mathematical space to represent user preferences
Use direction and magnitude to describe user preferences and calculate similarities
Apply cosine similarity to measure the angle between vectors and determine recommendations
Build a simple movie recommendation system using Python and a library like scikit-learn or TensorFlow
Test and evaluate the performance of the recommender system using metrics like precision and recall

Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding how trigonometry is applied in ML to build recommender systems, while software engineers can learn how to implement these concepts in code

Key Insight

💡 Trigonometry is used in machine learning to calculate similarities between vectors, enabling applications like recommender systems

Key Takeaways

Learn how trigonometry is used in machine learning to build a movie recommendation system using vectors and cosine similarity

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Title: Remember Trigonometry? Thats how you use it in ML

URL Source: https://medium.com/@nikj0113/remember-trigonometry-thats-how-you-use-it-in-ml-07da8a6b7680?source=rss------python-5

Published Time: 2026-04-25T00:30:17Z

Markdown Content:
# Remember Trigonometry? Thats how you use it in ML | by Nikhil Joshi | Apr, 2026 | Medium

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# Remember Trigonometry? Thats how you use it in ML

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Remember trigonometry?

Sine, cosine, angles… the stuff that once felt abstract, maybe even pointless?

Now imagine this: every time Netflix suggests a movie you end up loving, there’s a good chance **that same trigonometry is quietly at work behind the scenes**.

Not in the form of triangles on paper — but as **vectors, directions, and angles in a mathematical space**.

In this article, we’ll take a concept you already know and give it a completely new meaning:

* We’ll turn movies into **vectors you can visualize**
* Understand how **direction and magnitude describe preferences**
* See how **cosine similarity measures taste using angles**
* And finally, build a simple **movie recommendation system where all of this comes together**

_By the end, trigonometry won’t feel like a school topic anymore — it’ll feel like a tool that powers real-world intelligence._

**So lets see the structur

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Remember Trigonometry? Thats how you use it in ML

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