Linear Algebra for Data Science: Applications with Python
Learn key Linear Algebra techniques and how to implement from scratch in Python.
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What You Will Learn!
- Learn how to apply linear algebra techniques in Python to real world datasets.
- Learn how to implement PCA, Ordinary Least Squares, and Markov Chains from scratch.
- Improve your Python skills.
- Learn how Linear Algebra applies to Computer Vision, Search Engines, and Data Analysis.
Description
This comprehensive course on linear algebra for data science will teach you how to apply linear algebra concepts to various real-world data science problems. You will learn techniques like PCA (Principal Component Analysis), OLS (Ordinary Least Squares), Eigen Faces, Markov Chains, Page Rank, and the usage of linear algebra in Neural Networks and TF-IDF (Term Frequency-Inverse Document Frequency). By the end of this course, you will be equipped with the skills to use linear algebra to solve complex data science problems and make informed decisions based on your data.
Whether you're a beginner or an intermediate-level data scientist, this course is designed to give you a strong foundation in linear algebra and its applications to data science. It will help you to have already taken our previous Matrix Algebra and Linear Transformations & Vector Spaces courses. These courses will prime you for being able to truly follow along and understand both the theory & practice taught in this course. It is also helpful to have some experience with programming, preferably in Python so that you will be able to follow along with the code examples. We will be using Google Colab for our development environment so you will not have to worry about getting your own environment setup.
Get ready to unlock the power of linear algebra in your data science career!
Who Should Attend!
- Learners looking to build a career in Data Science
