Hi everyone!
My name is Aleksandar Samsiev and today I have an exciting announcement to make – we’ve just released a brand-new addition to the 365 Data Science Program: Linear Algebra and Feature Selection!
As the instructor of this course, I’m thrilled to share more details about the topics it covers, who can benefit from it, and what hands-on skills it will help you acquire. But first, let me say a few words about myself. I hold a Bachelor’s Degree in Mathematics from the University of Bath in the United Kingdom, where I graduated with honors. Apart from Mathematics, I’ve always been passionate about machine learning and AI – disciplines that have both evolved from the fundamentals we’re about to discuss.
To make Linear Algebra and Feature Selection as helpful and practical as possible, I collaborated with another 365 Data Science instructor and a friend of mine - Ivan Manov. Ivan is the author of our Dates and Times in Python course and working with him was both fun and rewarding, which I’m sure you can grasp watching the lessons.
So, let’s see what you can expect from the course itself!
The 365 Data Science Linear Algebra and Feature Selection Course: Why Linear Algebra?
Linear algebra is often neglected in data science courses, despite being critically important. Most instructors focus solely on specific frameworks and their practical application, leaving you with knowledge gaps and a lack of full understanding of the fundamentals. In this course, we provide you with the solid foundation you need to grasp complex ML and AI topics.
Linear Algebra and Feature Selection Course: Who Is It for?
The Linear Algebra and Feature Selection course is perfect for both aspiring and working professionals who want to level up their careers and add value to their company. The know-how of applying linear algebra to build your own algorithms or choose from a wide array of existing machine learning methods to solve specific tasks is beneficial for virtually any job role in data science.
What Will You Learn in This Course?
Under our guidance, you will learn:
✔️ Basic and advanced linear algebra notions
✔️ How to solve linear equations
✔️ Determine independency of a set of vectors
✔️ Calculate eigenvalues and eigenvectors
✔️ Principal Components Analysis
✔️ Linear Discriminant Analysis
✔️ How to perform Dimensionality Reduction in Python
✔️ How to compare the performance of PCA and LDA for classification with SVMs
The course also comprises two complete practical examples in Python. You will learn the mathematical concepts behind principal component analysis (PCA) and linear discriminant analysis (LDA), and how to apply them using the corresponding Python libraries. These examples will show you the inner workings of the algorithms and what happens when you operate with specific classes for dimensionality reduction.
Ready to learn Linear Algebra and Feature Selection?
Linear Algebra and Feature Selection course is part of the 365 Data Science Program, so enrolled students can access it at no extra cost.
Not a current subscriber? Visit the course page to find more details about its curriculum and try it out for free.
