Linear Algebra And Feature Selection In Python

Linear Algebra And Feature Selection In Python
Last updated 3/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Acquire the Theoretical and Practical Foundations That Would Allow You to Learn Machine Learning With Understanding

What you’ll learn

Understand the math behind machine learning models

Become familiar with basic and advanced linear algebra notions

Be able to solve linear equations

Determine independency of a set of vectors

Calculate eigenvalues and eigenvectors

Perform Linear Discriminant Analysis

Perform Dimensionality Reduction in Python

Carry out Principal Components Analysis

Compare the performance of PCA and LDA for classification with SVMs

Requirements

Suitable for beginners. Some understanding of Python basics and math would be an advantage.

Description

Overview

Section 1: Linear Algebra Essentials

Lecture 1 What Does The Course Cover

Lecture 2 Why Linear Algebra?

Lecture 3 Solving Quadratic Equations

Lecture 4 Vectors

Lecture 5 Matrices

Lecture 6 The Transpose of Vectors and Matrices, the Identity Matrix

Lecture 7 Linear Independence and Linear Span of Vectors

Lecture 8 Basis of a Vector Space, Determinant of a Matrix, Inverse of a Matrix

Lecture 9 Solving Equations of the Form Ax=b

Lecture 10 The Gauss Method

Lecture 11 Other Solutions to the Equation Ax=b

Lecture 12 Determining Linear Independence of a Random Set of Vectors

Lecture 13 Eigenvalues and Eigenvectors

Lecture 14 Calculating Eigenvalues

Lecture 15 Calculating Eigenvectors

Section 2: Dimensionality Reduction Motivation

Lecture 16 Feature Selection, Feature Extraction, and Dimensionality Reduction
Lecture 17 The Curse of Dimensionality

Section 3: Principal Component Analysis (PCA)

Lecture 18 An Overview of PCA

Lecture 19 A Step-by-Step Explanation of PCA on California Estates – Example

Lecture 20 The Theory Behind PCA

Section 4: Linear Discriminant Analysis (LDA)

Lecture 22 Overall Mean and Class Means

Lecture 23 An Overview of LDA

Lecture 24 LDA: Calculating the Within- and Between-Class Scatter Matrices

Lecture 25 A Step-by-Step Еxplanation of LDA on a Wine Quality Dataset – Example

Lecture 26 Calculating the Within- and Between-Class Scatter Matrices

Lecture 27 Calculating Eigenvectors and Eigenvalues for the LDA

Lecture 28 Analysis of LDA

Lecture 29 LDA vs. PCA

Lecture 30 Setting Up the Classifier to Compare LDA and PCA

Lecture 31 Coding the Classifier for LDA and PCA

Lecture 32 Analysis of the Training and Testing Times for the Classifier and Its Accuracy