Linear Algebra Programming with NumPy | CodeSignal Learn

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intermediate

Linear Algebra Programming with NumPy

Data Science & Analytics

3 courses

75 practices

10 hours

This course introduces linear algebra concepts using NumPy, focusing on practical applications. It reinforces key topics like vectors, matrices, and systems of equations, with hands-on exercises to help you apply linear algebra in data science and machine learning.

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Verified skills you'll gain

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Coding and Data Algorithms

INTERMEDIATE

Mathematics and Statistics

Tools you'll use

Numpy

Python

Course 1

Fundamentals of Vectors and Matrices with NumPy

5 lessons

25 practices

This course introduces the basics of vectors and matrices, including initialization, essential properties, and an introduction to the Numpy Linalg library, to build a foundation for linear algebra operations using NumPy.

Course 2

Vector and Matrix Operations with NumPy

5 lessons

25 practices

This course covers essential vector and matrix operations such as addition, subtraction, and multiplication. Students will gain practical experience with linear algebra techniques, further developing their skills with matrix manipulation and foundational operations.

Course 3

Eigenvalues, Eigenvectors, and Diagonalization with NumPy

5 lessons

25 practices

This course introduces students to eigenvalues, eigenvectors, and matrix diagonalization. Focusing on matrix transformations, students will explore practical applications of these concepts using the `numpy.linalg` library to solidify their understanding of advanced linear algebra principles.

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