Math for Data Science & Machine Learning

Data Science    |    Intermediate
  • 14 videos | 1h 1m 26s
  • Includes Assessment
  • Earns a Badge
Rating 3.9 of 177 users 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

  • 1m 35s
  • 4m 30s
    Upon completion of this video, you will be able to understand how to work with vectors in Python. FREE ACCESS
  • Locked
    3.  Basis and Projection of Vectors
    5m 9s
    Upon completion of this video, you will be able to understand the basis and projection of vectors in Python. FREE ACCESS
  • Locked
    4.  Work with Matrices
    3m 18s
    After completing this video, you will be able to understand how to work with matrices in Python. FREE ACCESS
  • Locked
    5.  Matrix Multiplication
    4m 20s
    Upon completion of this video, you will be able to understand how to multiply matrices using Python. FREE ACCESS
  • Locked
    6.  Matrix Division
    4m 18s
    After completing this video, you will be able to understand how to divide matrices by scalars in Python. FREE ACCESS
  • Locked
    7.  Linear Transformations
    3m 18s
    Upon completion of this video, you will be able to understand how to work with linear transformations in Python. FREE ACCESS
  • Locked
    8.  Gaussian Elimination
    4m 48s
    After completing this video, you will be able to understand how to apply Gaussian elimination in Python. FREE ACCESS
  • Locked
    9.  Determinants
    5m 2s
    Upon completion of this video, you will be able to understand how to work with determinants in Python. FREE ACCESS
  • Locked
    10.  Orthogonal Matrices
    4m 42s
    After completing this video, you will be able to understand how to work with orthogonal matrices in Python. FREE ACCESS
  • Locked
    11.  Eigenvalues
    5m 33s
    Upon completion of this video, you will be able to recognize how to obtain eigenvalues from eigen decomposition in Python. FREE ACCESS
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    12.  Eigenvectors
    4m 17s
    Upon completion of this video, you will be able to recognize how to obtain eigenvectors from eigen decomposition in Python. FREE ACCESS
  • Locked
    13.  Pseudo Inverse
    3m 56s
    After completing this video, you will be able to recognize how to obtain the pseudo inverse in Python. FREE ACCESS
  • Locked
    14.  Exercise: Math for Data Science and Machine Learning
    6m 41s
    Learn how to use math for data science and machine learning. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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