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What is Vectorization in Machine Learning?

Jalal Mansoori
Towards Data Science
4 min readJun 12, 2020

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Make your code execute fast using vectorization

What you’ll learn :

  1. What is Vectorization?
  2. How Vectorization is important in Machine learning?
  3. Example: Unvectorized Vs Vectorized Implementation
  4. Advantages of Vectorized Implementation
  5. Demonstration on jupyter notebook

The first time when I learned about the concept of Vectorization it was when I was learning the most well-known course Machine learning by Prof Andrew Ng on the Coursera platform.

According to Prof Andrew Ng :

“ The ability to perform Vectorization has become a key skill

So let’s learn this skill and include it into our tools box :)

What is Vectorization?

Vectorization is a technique by which you can make your code execute fast. It is a very interesting and important way to optimize algorithms when you are implementing it from scratch.

Now, with the help of highly optimized numerical linear algebra libraries in C/C++, Octave/Matlab, Python, …etc. We can make our code run efficiently.

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Towards Data Science
Towards Data Science

Published in Towards Data Science

Your home for data science and AI. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.

Jalal Mansoori
Jalal Mansoori

Written by Jalal Mansoori

I am a Computer Science graduate and I find myself somewhere at the intersection of Learn, Create, and Share. https://medium.com/@jalalmansoori/membership

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