Linear Algebra and Feature Selection in Python 2022
Acquire the Theoretical and Practical Foundations That Would Allow You to Learn Machine Learning With Understanding
This course will allow you to become a professional who understands the math on which algorithms are built, rather than someone who applies them blindly without knowing what happens behind the scenes.
Linear algebra is often overlooked in data science courses, despite being of paramount importance. Most instructors tend to focus on the practical application of specific frameworks rather than starting with the fundamentals, which leaves you with knowledge gaps and a lack of full understanding. In this course, we give you an opportunity to build a strong foundation that would allow you to grasp complex ML and AI topics.
The course starts by introducing basic algebra notions such as vectors, matrices, identity matrices, the linear span of vectors, and more. We’ll use them to solve practical linear equations, determine linear independence of a random set of vectors, and calculate eigenvectors and eigenvalues, all preparing you for the second part of our learning journey – dimensionality reduction.
Best Seller Course: Master Math by Coding in Python
What you’ll learn
- Understand the math behind machine learning models
- Become familiar with basic and advanced linear algebra notions
- Be able to solve linear equations
- Determine independency of a set of vectors
- Calculate eigenvalues and eigenvectors
- Perform Linear Discriminant Analysis
- Perform Dimensionality Reduction in Python
- Carry out Principal Components Analysis
- Compare the performance of PCA and LDA for classification with SVMs
You May Also Need This Course: Machine Learning: Natural Language Processing in Python (V2)
