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


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https://www.udemy.com/course/linear-algebra-for-data-science-techniques-and-applications/