Linear Algebra and Feature Selection

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

4.7/5

(371)

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

3 hours of content 4933 students

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What you get:

3 hours of content
42 Interactive exercises
8 Coding exercises
13 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Linear Algebra and Feature Selection

A course by Aleksandar Samsiev and Ivan Manov

Start for Free

What you get:

3 hours of content
42 Interactive exercises
8 Coding exercises
13 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

$99.00

Lifetime access

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Start for Free

What you get:

3 hours of content
42 Interactive exercises
8 Coding exercises
13 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Understand the math behind machine learning and AI models
Master key linear algebra concepts and theories for advanced mathematical applications
Perform Principal Component Analysis (PCA) and Dimensionality Reduction in Python to simplify complex datasets for better analysis
Learn to operate with eigenvalues and eigenvectors
Apply theoretical math knowledge into practice to solve real-world problems through quantitative analysis
Understand why linear algebra is useful in AI

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

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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

Curriculum

1. Linear Algebra Essentials

23 Lessons 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
4 min
Why Linear Algebra?
3 min
The case of mysterious model failure Read now
2 min
Solving quadratic equations Read now
4 min
Why quadratic equations are more than just a formula Read now
5 min
Vectors
5 min
Vector addition: Geometric view Read now
1 min
Matrices
4 min
Matrix transformation: Geometric view Read now
1 min
Vectors and matrices: What are they in AI and ML Read now
3 min
The Transpose of Vectors and Matrices, the Identity Matrix
4 min
Why the Identity matrix matters in AI Read now
3 min
Linear Independence and Linear Span of Vectors
7 min
Smart features, smarter models: How linear independence fuels AI Read now
3 min
Basis of a Vector Space, Determinant of a Matrix, Inverse of a Matrix
10 min
Basis, Determinant, and inverse — the backbone of machine learning math Read now
3 min
Solving Equations of the Form A*x=b
6 min
The Gauss Method
7 min
Other Solutions to the Equation A*x=b
8 min
Determining Linear Independence of a Random Set of Vectors
4 min
Eigenvalues and Eigenvectors
3 min
Calculating Eigenvalues
4 min
Calculating Eigenvectors
6 min
2. Dimensionality Reduction Motivation

2 Lessons 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
4 min
The Curse of Dimensionality
3 min
3. Principal Component Analysis (PCA)

4 Lessons 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
7 min
A Step-by-Step Explanation of PCA on California Estates – Example
12 min
The Theory Behind PCA
5 min
PCA Covariance Matrix in Jupyter – Analysis and Interpretation
7 min
4. Linear Discriminant Analysis (LDA)

11 Lessons 60 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
3 min
Linear Discriminant Analysis – Overview
4 min
LDA: Calculating Within- and Between-Class Scatter Matrices
9 min
A Step-by-Step Еxplanation of LDA on a Wine Quality Dataset – Example
6 min
Calculating the Within- and Between-Class Scatter Matrices
4 min
Calculating Eigenvectors and Eigenvalues for the LDA
7 min
Analysis of LDA
3 min
LDA vs. PCA
5 min
Setting Up the Classifier to Compare LDA and PCA
5 min
Coding the Classifier for LDA and PCA
6 min
Analysis of the Training and Testing Times for the Classifier and Its Accuracy
8 min

Topics

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

Tools & Technologies

Course Requirements

You need to complete an introduction to Python before taking this course
Basic skills in statistics, probability, and linear algebra are required
It is highly recommended to take the Machine Learning in Python course first

Who Should Take This Course?

Level of difficulty: Intermediate

People who want to improve their math for data science
Aspiring data analysts, data scientists, business analysts, and AI engineers
Graduate students who need linear algebra and calculus for their studies

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Aleksandar Samsiev

Machine Learning Engineer at

1 Courses

371 Reviews

4933 Students

Aleksandar has a Bachelor's in Mathematics from the University of Bath (England), where he graduated with honors. He discovered a true passion in artificial intelligence, and believes that “a firm grasp of Linear Algebra is fundamental for understanding and developing concepts in the machine learning field”. In 2021, Aleksandar authored our Linear Algebra and Feature Selection course, while also contributing to the creation process of exam questions for the 365 Data Science Program. In his spare time, Aleksandar is an avid football supporter and a keen swimmer with 8 years of national-level competitive swimming under his belt.