# Maths for Data Science by DataTrained

Maths for Data Science by DataTrained

Maths for Data Science by DataTrained Explore the application of key mathematical topics related to linear algebra with the Python programming language

Explore the application of key mathematical topics related to linear algebra with the Python programming language

Requirements

- 10th class Level math knowledge is expected

Description

Overview: Explore the application of key mathematical topics related to linear algebra with the Python programming language.

Expected Duration: After completion of this course, you should be able to accomplish the objectives from the following lessons and topics.

- 1. Lessons on Math for Data Science & Machine Learning:
- 2. Understand how to work with vectors in Python
- 3. Basis and Projection of Vectors: Understand the Basis and Projection of Vectors in Python
- 4. Work with Matrices: Understand how to work with matrices in Python
- 5. Matrix Multiplication: Understand how to multiply matrices in Python
- 6. Matrix Division: Understand how to divide matrices in Python
- 7. Linear Transformations: Understand how to work with linear transformations in Python
- 8. Gaussian Elimination: Understand how to apply Gaussian Elimination
- 9. Determinants: Understand how to work with determinants in Python
- 10. Orthogonal Matrices: Understand how to work with orthogonal matrices in Python
- 11. Eigenvalues: Recognize how to obtain eigenvalues from eight decompositions in Python
- 12. Eigenvectors: Recognize how to obtain eigenvectors from eigendecomposition in Python
- 13. PseudoInverse: Recognize how to obtain pseudoinverse in Python

Who this course is for:

- Beginner python developers looking for a data science career

## Post a Comment for "Maths for Data Science by DataTrained"