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Linear Algebra with Python Next Steps: Essential Math for Machine Learning—with Interactivity

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Learn the fundamental math behind common machine learning models

This live event utilizes Jupyter Notebook technology

Linear algebra is the branch of mathematics dealing with linear equations and their representations in vector spaces and through matrices. Much of machine learning is built on top of linear algebra, making it an important skill for data scientists to know.

Join expert Tatiana Ediger to go beyond linear algebra fundamentals and dive into machine learning applications. You’ll explore inner products and norms (which are crucial for understanding and evaluating many machine learning models) and learn about matrix decomposition (which is useful when working with complex structural data and implementing different machine learning algorithms). With these key linear algebra skills, you’ll be able to understand and apply various data science algorithms.

What you’ll learn and how you can apply it

By the end of this live online course, you’ll understand:

  • Inner products and norms and how they’re used to evaluate predictive models
  • How to decompose matrices in two ways to solve problems in machine learning

And you’ll be able to:

  • Compute inner products and norms
  • Apply eigendecomposition and singular value decomposition to a matrix
  • Use Python’s NumPy framework to perform more advanced linear algebra operations

This live event is for you because...

  • You hold a technical role but are looking to transition to a data scientist position.
  • You work with data and want to build and understand predictive models.
  • You want to become a data scientist and work with machine learning.

Prerequisites

  • A basic understanding of linear algebra (representing data as a vector or matrix, setting up a system of linear equations, etc.)
  • A working knowledge of Python (variable creation, conditional statements, functions, loops, etc.)
  • Experience using Python’s NumPy library

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Linear algebra recap (15 minutes)

  • Presentation: Linear algebra basics—systems of equations, operations of vectors and matrices; linear algebra basics with NumPy
  • Jupyter notebook exercise: Operations with NumPy arrays (matrix multiplication and transposition)

Special types of matrices (30 minutes)

  • Presentation: Identity and inverse matrices; diagonal and symmetric matrices
  • Jupyter notebook exercises: Find an inverse matrix; identify which matrices have special properties
  • Q&A

Break (5 minutes)

Inner products and norms (30 minutes)

  • Presentation: Dot product and orthogonality; norms
  • Jupyter notebook exercises: Dot product on NumPy; find L1 and L2 norms
  • Q&A

Eigenvalues and eigenvectors (15 minutes)

  • Presentation: Eigendecomposition
  • Jupyter notebook exercise: Use NumPy to find eigenvalues and eigenvectors

Singular value decomposition (25 minutes)

  • Presentation: SVD; SVD in Python
  • Jupyter notebook exercise: Find the SVD using code
  • Q&A

Your Instructor

  • Tatiana Ediger

    Tatiana Ediger is a software engineer at WHOOP. Previously, she helped teach the Fundamentals of Computer Science course for two years at Northeastern University and conducted research with Northeastern professors in the field of machine learning and data privacy. Tatiana has a passion for data science as the intersection of math and computer science.

Skills covered

  • Linear Algebra
  • Machine Learning
  • Python