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Numpy Basics For Machine Learning


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
 Description

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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