Emmanuel O.
Before 365:
Business administration graduate
After 365:
Entry level Data Scientist
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Online Course
free
Linear Algebra and Feature Selection
with
Aleksandar Samsiev
and
Ivan Manov
Build the fundamental and practical linear algebra skills needed to become a data scientist and work on machine learning models and AI
5400 students already have enrolled
Skill level:
Intermediate
Duration:
3 hours
-
Lessons (3 hours)
-
Projects (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
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
Machine Learning Engineer at Empower Solutions
Track record
Bringing real-world expertise from leading global companies
Academic background
Bachelor's degree, Mathematics
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
Course OVERVIEW
Description
CPE Credits: 5.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
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
- 2. Dimensionality Reduction Motivation7 MinThis section explains the intricacies of the dimensionality reduction process and clarifies why this technique is essential when working with large datasets.
- 3. Principal Component Analysis (PCA)31 MinIn 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.
- 4. Linear Discriminant Analysis (LDA)36 MinIn 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 FreeLinear Discriminant Analysis – Overview FreeLDA: Calculating Within- and Between-Class Scatter Matrices FreeA Step-by-Step Еxplanation of LDA on a Wine Quality Dataset – Example FreeExercise FreeCoding exercise FreeCalculating the Within- and Between-Class Scatter Matrices FreeExercise FreeCoding exercise FreeCoding exercise FreeCalculating Eigenvectors and Eigenvalues for the LDA FreeCoding exercise FreeAnalysis of LDA FreeCoding exercise Free
- 5. LDA vs PCA24 Min
- 6. Course project and exam245 Min
Free preview
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
9 in 10
people walk away career-ready
with practical data and AI skills.
96%
of our students recommend
365 Data Science.
94%
of AI and data science graduates
successfully change
or advance their careers.
ACCREDITED certificates
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How it WORKS
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