- Understand how to work with vectors in Python
- Understand the Basis and Projection of Vectors in Python
- Work with Matrices
- Matrix Multiplication
- Matrix Division
- Linear Transformations
- Gaussian Elimination
- Orthogonal Matrices
- Eigen values
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
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About the instructors
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Data Scientist and HR Head at DataTrained
I regard myself as a divine professional and have got 6 years of experience into HR and have got more than 7 years of experience in HR Analytics. I am heading the HR analytics team at DataTrained and is also heading the HR team.
This course has been designed for the students who want to brush up their basics on math usage in data science using Python as a tool.
Notebooks which are used for explanation is missing sir, It will be very helpful if u can share all the notebooks.