Linear Algebra For Data Science: Techniques And Applications

Learn key Linear Algebra techniques and how to implement from scratch in Python.

What you'll 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.

Requirements

Understanding of common matrix operations & linear transformations.

Some programming experience, preferably in Python.

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!

Overview

Section 1: Principal Component Analysis

Lecture 1 Principal Component Analysis: Overview

Lecture 2 Mean-centering & Standardization

Lecture 3 Covariance Matrix

Lecture 4 PCA: Eigen Decomposition Overview

Lecture 5 PCA: Eigen Decomp (Visual Explanation)

Lecture 6 Notes on Google Colaboratory

Lecture 7 PCA: Eigen Decomp (Code Walkthrough)

Lecture 8 PCA: Singular Value Decomposition Overview

Lecture 9 PCA: Singular Value Decomp - 2x2 Concrete Example

Lecture 10 PCA: Singular Value Decomp - Code Walkthrough

Lecture 11 PCA: Real World Example

Lecture 12 PCA: Summary

Lecture 13 Code for PCA

Section 2: Ordinary Least Squares

Lecture 14 Ordinary Least Squares (OLS): Overview

Lecture 15 OLS: Derivation

Lecture 16 OLS: Visual Intuition

Lecture 17 OLS: 3D Concrete Example

Lecture 18 OLS: Small Example In Python

Lecture 19 OLS: Checking Model Assumptions

Lecture 20 OLS: Summary

Lecture 21 Code for OLS

Section 3: Eigen Faces: Facial Recognition Application

Lecture 22 Eigen Faces: Overview

Lecture 23 Eigen Faces: Algorithmic Deep-Dive

Lecture 24 Eigen Faces: Python Implementation

Lecture 25 Eigen Faces: Summary

Lecture 26 Code for Eigen Faces Project

Section 4: Markov Chains

Lecture 27 Markov Chains: Overview

Lecture 28 Markov Chains: Operations & Properties

Lecture 29 Markov Chains: Concrete Example

Lecture 30 Markov Chains: Python Implementation

Lecture 31 Markov Chains: Summary

Lecture 32 Code For Markov Chains

Section 5: Page Rank: Markov Chain application

Lecture 33 Page Rank: Introduction

Lecture 34 Page Rank: Concrete Example

Lecture 35 Page Rank: Example In Python

Lecture 36 Page Rank: Summary

Lecture 37 Page Rank Code

Section 6: Deep Learning & Natural Language Processing

Lecture 38 Neural Networks

Lecture 39 Natural Language Processing: Overview

Lecture 40 NLP: TF-IDF Algorithm Explained

Lecture 41 NLP: TF-IDF Python Implementation

Lecture 42 Section Summary & Next Steps

Lecture 43 Code for TF-IDF

Learners looking to build a career in Data Science