Online Course
Linear Algebra and Feature Selection

Name: Linear Algebra and Feature Selection Course
Price: 36 USD

Build the fundamental and practical linear algebra skills needed to become a data scientist and work on machine learning models and AI

4.8

863 reviews on

Start for free

6,857 students already enrolled

Skill level:

Intermediate

Duration:

3 hours

Lessons (3 hours)

Projects (3 hours)

CPE credits:

5.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Understand the math behind machine learning and AI models.
Master key linear algebra concepts for advanced applications.
Perform PCA and dimensionality reduction in Python.
Operate with eigenvalues and eigenvectors confidently.
Apply math knowledge to solve real-world quantitative problems.

Topics & tools

PythonData AnalysisMachine LearningMathematicsPrincipal Component Analysis (PCA)Linear Discriminant Analysis (LDA)Dimensionality ReductionArtificial IntelligenceMath & StatisticsData PreprocessingMachine and Deep LearningTheory

Your instructor

Aleksandar Samsiev

Machine Learning Engineer at Empower Solutions

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, Mathematics

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Ivan Manov

AI & Data Science Course Creator | Sound Engineer

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Sound Engineering

Media and recognition

Author of Speech Recognition with Python on 365DS & Udemy
Speaker at Power Platform Bootcamp 2025 on Audio AI
Sound Engineer for TV/Film incl. The Bachelor & award-winners

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Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 5.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Linear Algebra and Feature Selection is the course that provides you with the knowledge you need to grasp the math processes behind the machine learning algorithms for dimensionality reduction. Mastering the fundamentals of linear algebra will help you develop in-demand practical skills, such as building your own algorithms or choosing the most appropriate existing ones for a specific task you need to solve. The techniques you will learn - feature extraction and feature selection will enable you to handle high-dimensional data efficiently. In addition, you will get familiar with the mathematical concepts behind PCA and LDA, and practice applying these types of analysis using the corresponding Python libraries.

Prerequisites

Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
Intermediate Python skills are required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

40 lessons 49 exercises 1 project 1 exam

1. Linear Algebra Essentials

100 min

Here, we’ll cover the linear algebra concepts behind the machine learning algorithms for dimensionality reduction. We'll learn about vectors and matrices, linear equations, eigenvalues and eigenvectors, and more.

100 min

Here, we’ll cover the linear algebra concepts behind the machine learning algorithms for dimensionality reduction. We'll learn about vectors and matrices, linear equations, eigenvalues and eigenvectors, and more.

What Does the Course Cover Free

Why Linear Algebra? Free

The case of mysterious model failure

Solving quadratic equations Free

Why quadratic equations are more than just a formula

Vectors Free

Vector addition: Geometric view

Matrices Free

Matrix transformation: Geometric view

Vectors and matrices: What are they in AI and ML

Exercise

The Transpose of Vectors and Matrices, the Identity Matrix Free

Why the Identity matrix matters in AI

Linear Independence and Linear Span of Vectors Free

Smart features, smarter models: How linear independence fuels AI

Basis of a Vector Space, Determinant of a Matrix, Inverse of a Matrix Free

Basis, Determinant, and inverse — the backbone of machine learning math

Exercise

Solving Equations of the Form A*x=b Free

The Gauss Method Free

Exercise

Other Solutions to the Equation A*x=b Free

Determining Linear Independence of a Random Set of Vectors

Exercise

Eigenvalues and Eigenvectors

Calculating Eigenvalues

Calculating Eigenvectors

Exercise

Exercise
2. Dimensionality Reduction Motivation

7 min

This section explains the intricacies of the dimensionality reduction process and clarifies why this technique is essential when working with large datasets.

7 min

This section explains the intricacies of the dimensionality reduction process and clarifies why this technique is essential when working with large datasets.

Feature Selection, Feature Extraction, and Dimensionality Reduction

The Curse of Dimensionality

Exercise

Exercise
3. Principal Component Analysis (PCA)

31 min

In this part of the course, we explore the Principal Component Analysis (PCA) - one of the most widely used algorithms for dimensionality reduction. We'll demonstrate a practical example combining both feature extraction and feature selection techniques to achieve the desired goal - reducing the number of dimensions in our dataset.

31 min

In this part of the course, we explore the Principal Component Analysis (PCA) - one of the most widely used algorithms for dimensionality reduction. We'll demonstrate a practical example combining both feature extraction and feature selection techniques to achieve the desired goal - reducing the number of dimensions in our dataset.

Principal Component Analysis – Overview

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

Exercise

Coding exercise

The Theory Behind PCA

PCA Covariance Matrix in Jupyter – Analysis and Interpretation

Exercise

Coding exercise

Exercise
4. Linear Discriminant Analysis (LDA)

36 min

In this section, we'll cover another dimensionality reduction technique called Linear Discriminant Analysis (LDA). Here, we'll go through another practical example, showing the methodology behind LDA and its efficiency. We'll also make a comparison between this algorithm and PCA, introducing the advantages of both approaches.

36 min

In this section, we'll cover another dimensionality reduction technique called Linear Discriminant Analysis (LDA). Here, we'll go through another practical example, showing the methodology behind LDA and its efficiency. We'll also make a comparison between this algorithm and PCA, introducing the advantages of both approaches.

Overall Mean and Class Means

Linear Discriminant Analysis – Overview

LDA: Calculating Within- and Between-Class Scatter Matrices

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

Exercise

Coding exercise

Calculating the Within- and Between-Class Scatter Matrices

Exercise

Coding exercise

Coding exercise

Calculating Eigenvectors and Eigenvalues for the LDA

Coding exercise

Analysis of LDA

Coding exercise
5. LDA vs PCA

24 min

24 min

LDA vs. PCA

Exercise

Setting Up the Classifier to Compare LDA and PCA

Coding the Classifier for LDA and PCA

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

Exercise

Exercise
6. Course project and exam

245 min

245 min

Music Genre Classification Project with PCA and Logistic Regression

Course exam

Free lessons

1.1 What Does the Course Cover

4 min

1.2 Why Linear Algebra?

3 min

1.4 Solving quadratic equations

4 min

1.6 Vectors

5 min

1.8 Matrices

4 min

1.12 The Transpose of Vectors and Matrices, the Identity Matrix

4 min

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