Linear Algebra and Feature Selection

with Aleksandar Samsiev and Ivan Manov

Providing you with the theoretical and practical foundations you need to apply machine learning techniques with confidence and understanding.

3 hours 32 lessons
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Course Overview

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.

32 High Quality Lessons
0 Practical Tasks
3 Hours of Video
Certificate of Achievement

Topics covered

Discover the linear algebra techniques applied in machine learning algorithms.Explore LDA - a supervised machine learning algorithm for dimensionality reduction.Focus on the concepts behind dimensionality reduction and learn why is this technique so important.Learn about PCA - one of the most widely used algorithms for dimensionality reduction.

What You'll Learn

Working with machine learning is not only about applying algorithms. It’s about understanding their inner workings and how they function. This course gives you insight into the dimensionality reduction algorithms PCA and LDA and explains the math processes behind them.

Understand the math behind machine learning models
Become capable of solving linear equations
Calculate eigenvalues and eigenvectors
Become familiar with basic and advanced linear algebra notions
Determine independency of a set of vectors
Carry out Principal Components Analysis
Perform Linear Discriminant Analysis
Perform Dimensionality Reduction in Python
Compare the performance of PCA and LDA for classification with SVMs


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the teacher explains too fast and does not gives you time to process information. Also, I think there should be a wider explanation on Eigenvectors, Eigenvalues and linear span.
Great course, I like that there is not only the theory, but also practice tasks.
An important mathematical intelligence prior to Machine Learning. Great job!
I think some additional visualization would be good for explanation process
Great Explanation of the practical material used in ML.
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“Linear algebra and feature selection have shaped the world of machine learning and can be extremely useful in identifying the most efficient approach to manipulating data. This course will help you understand the mathematical concepts behind ML and how to integrate them into the feature selection activity.”

Aleksandar Samsiev
Worked at 365
Linear Algebra and Feature Selection

with Aleksandar Samsiev and Ivan Manov

Start Course