### Math for Data Science & Machine Learning

**Overview/Description**

**Expected Duration**

**Lesson Objectives**

**Course Number**

**Expertise Level**

**Overview/Description**

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.

**Expected Duration (hours)**

1.0

**Lesson Objectives**

**Math for Data Science & Machine Learning**

**Course Number:**

it_mlmds1dj_01_enus

**Expertise Level**

Intermediate