Math for Data Science & Machine Learning

Data Science    |    Intermediate
• 14 videos | 1h 1m 26s
• Includes Assessment
Rating 3.9 of 177 users (177)
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

• Upon completion of this video, you will be able to understand how to work with vectors in Python.
• 3.  Basis and Projection of Vectors
Upon completion of this video, you will be able to understand the basis and projection of vectors in Python.
• 4.  Work with Matrices
After completing this video, you will be able to understand how to work with matrices in Python.
• 5.  Matrix Multiplication
Upon completion of this video, you will be able to understand how to multiply matrices using Python.
• 6.  Matrix Division
After completing this video, you will be able to understand how to divide matrices by scalars in Python.
• 7.  Linear Transformations
Upon completion of this video, you will be able to understand how to work with linear transformations in Python.
• 8.  Gaussian Elimination
After completing this video, you will be able to understand how to apply Gaussian elimination in Python.
• 9.  Determinants
Upon completion of this video, you will be able to understand how to work with determinants in Python.
• 10.  Orthogonal Matrices
After completing this video, you will be able to understand how to work with orthogonal matrices in Python.
• 11.  Eigenvalues
Upon completion of this video, you will be able to recognize how to obtain eigenvalues from eigen decomposition in Python.
• 12.  Eigenvectors
Upon completion of this video, you will be able to recognize how to obtain eigenvectors from eigen decomposition in Python.
• 13.  Pseudo Inverse
After completing this video, you will be able to recognize how to obtain the pseudo inverse in Python.
• 14.  Exercise: Math for Data Science and Machine Learning
Learn how to use math for data science and machine learning.

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