# Math for Data Science & Machine Learning

Data Science    |    Intermediate
• 14 Videos | 1h 7m 26s
• Includes Assessment
Likes 67
Explore the machine learning application of key mathematical topics related to linear algebra with the Python programming language in this 13-video course. The programming demonstrated in this course requires access to Python Jupyter, and requires a Python 3 Jupyter kernel. First, you will learn to work with vectors, ordered lists of numbers, in Python, and then examine how to use Python's NumPy library when working with linear algebra. Next, you will enlist the NumPy library and the array object to create a vector. Learners will continue by learning how to use the NumPy library to create a matrix, a multidimensional array, or a list of vectors. Then examine matrix multiplication and division, and linear transformations. You will learn how to use Gaussian elimination determinants and orthogonal matrices to solve a system of linear equations. This course examines the concepts of eigenvalues, eigenvectors, and eigendecomposition, a factorization of a matrix into a canonical form. Finally, you will learn how to work with pseudo inverse in Python.

## WHAT YOU WILL LEARN

• understand how to work with vectors in Python understand basis and projection of vectors in Python understand how to work with matrices in Python understand how to multiply matrices in Python understand how to divide matrices in Python understand how to work with linear transformations in Python understand how to apply gaussian elimination in Python
• understand how to work with determinants in Python understand how to work with orthogonal matrices in Python recognize how to obtain eigenvalues from eigen decomposition in Python recognize how to obtain eigenvectors from eigen decomposition in Python recognize how to obtain pseudo inverse in Python work with math for data science and machine learning

## IN THIS COURSE

• 1.
Course Overview
• 2.
Work with Vectors
• 3.
Basis and Projection of Vectors
• 4.
Work with Matrices
• 5.
Matrix Multiplication
• 6.
Matrix Division
• 7.
Linear Transformations
• 8.
Gaussian Elimination
• 9.
Determinants
• 10.
Orthogonal Matrices
• 11.
Eigenvalues
• 12.
Eigenvectors
• 13.
Pseudo Inverse
• 14.
Exercise: Math for Data Science and Machine Learning

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