Linear Algebra and Feature Selection in Python
Acquire the Theoretical and Practical Foundations That Would Allow You to Learn Machine Learning With Understanding
Created by 365 Careers | 3 hours on-demand video course
Do you want to learn linear algebra? You have come to the right place! First and foremost, we want to congratulate you because you have realized the importance of obtaining this skill. Whether you want to pursue a career in data science, machine learning, data analysis, software engineering, or statistics, you will need to know how to apply linear algebra. 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.
In this course, we’ll discuss two staple techniques for dimensionality reduction – Principal Components Analysis (PCA), and Linear Discriminant Analysis (LDA). These methods transform the data you work with and create new features that carry most of the variance related to a given dataset. First, you will learn the theory behind PCA and LDA. Then, going through two complete examples in Python, you will see how data transformation occurs in practice. For this purpose, you will get one step-by-step application of PCA and one of LDA. Finally, we will compare the two algorithms in terms of speed and accuracy.
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
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