Numpy Basics For Machine Learning

Learn the basics of scientific computing package used by many Data Scientists.

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.

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.

Course Content

  • Numpy Basics.
  • Arrays and Vectors.
  • Matrix Operations.
  • Indexing and Slicing.
  • Use Case: Euclidean Distance.

Numpy Basics For Machine Learning

Requirements

  • Some programming experience.
  • A zeal to learn.
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.

Who this course is for:
  • Beginner Data Science
  • Python programmers
  • Any programmers interested in machine learning
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