# Numpy Basics For Machine Learning

Numpy Basics For Machine Learning | Udemy | NED

*start your data science career with the essentials of*

**Numpy**for strong foundation for understanding**machine****learning**algorithms from a coding perspective. We will cover**basics**of**Numpy**like arrays, vectors, matrix operations and also have a use case in calculating Euclidean distance. What you’ll learn.**Basics**of**Numpy**and linear algebra.by Srikanth Potukuchi

What you'll learn

- Basics of Numpy and linear algebra
- Python for data science
- Better prepared for learning machine learning
- Practice on Jupyter notebook or Google colab

If you are looking to become a data scientist, it is essential to learn linear algebra and what better way to learn it than by using Numpy as Python package that is so powerful that it was used to build sklearn(most popular machine learning package). Kick -start your data science career with the essentials of Numpy for strong foundation for understanding machine learning algorithms from a coding perspective. We will cover basics of Numpy like arrays, vectors, matrix operations and also have a use case in calculating Euclidean distance.

Lets get started quickly. **Numpy**
is a math library for python. It enables us to do computation
efficiently and effectively. It is better than regular python because of
it’s amazing capabilities. In this article I’m just going to introduce
you to the **basics** of what is mostly required for **machine** **learning** and datascience.

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