Linear Algebra and Feature Selection in Python

Linear Algebra and Feature Selection in Python, available at $54.99, has an average rating of 4.67, with 32 lectures, based on 706 reviews, and has 2863 subscribers.

You will learn about 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 This course is ideal for individuals who are Ideal for beginner data science and machine learning students or Aspiring data analysts or Aspiring data scientists or Aspiring machine learning engineers or People who want to level-up their career and add value to their company or Anyone who wants to start a career in data science or machine learning It is particularly useful for Ideal for beginner data science and machine learning students or Aspiring data analysts or Aspiring data scientists or Aspiring machine learning engineers or People who want to level-up their career and add value to their company or Anyone who wants to start a career in data science or machine learning.

Enroll now: Linear Algebra and Feature Selection in Python

Summary

Title: Linear Algebra and Feature Selection in Python

Price: $54.99

Average Rating: 4.67

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Ideal for beginner data science and machine learning students

Aspiring data analysts

Aspiring data scientists

Aspiring machine learning engineers

People who want to level-up their career and add value to their company

Anyone who wants to start a career in data science or machine learning

Target Audiences

Ideal for beginner data science and machine learning students

Aspiring data analysts

Aspiring data scientists

Aspiring machine learning engineers

People who want to level-up their career and add value to their company

Anyone who wants to start a career in data science or machine learning

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.

But let’s answer a pressing question you probably have at this point:

“What can I expect from this course and how it will help my professional development?”

In brief, we will provide you with the theoretical and practical foundations for two fundamental parts of data science and statistical analysis – linear algebra and dimensionality reduction.

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.

The concept of dimensionality reduction is crucial in data science, statistical analysis, and machine learning. This isn’t surprising, as the ability to determine the important features in a dataset is essential – especially in today’s data-driven age when one must be able to work with very large datasets.

Imagine you have hundreds or even thousands of attributes in your data. Working with such complex information could lead to a variety of problems – slow training time, the possibility of multicollinearity, the curse of dimensionality, or even overfitting the training data.

Dimensionality reduction can help you avoid all these issues, by selecting the parts of the data which actually carry important information and disregarding the less impactful ones.

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.

We’ve put a lot of effort to make this course the perfect foundational training for anyone who wants to become a data analyst, data scientist, or machine learning engineer.

Course Curriculum

Chapter 1: Linear Algebra Essentials

Lecture 1: What Does The Course Cover

Lecture 2: Why Linear Algebra?

Lecture 3: Solving Quadratic Equations

Lecture 4: Vectors

Lecture 5: Matrices

Lecture 6: The Transpose of Vectors and Matrices, the Identity Matrix

Lecture 7: Linear Independence and Linear Span of Vectors

Lecture 8: Basis of a Vector Space, Determinant of a Matrix, Inverse of a Matrix

Lecture 9: Solving Equations of the Form Ax=b

Lecture 10: The Gauss Method

Lecture 11: Other Solutions to the Equation Ax=b

Lecture 12: Determining Linear Independence of a Random Set of Vectors

Lecture 13: Eigenvalues and Eigenvectors

Lecture 14: Calculating Eigenvalues

Lecture 15: Calculating Eigenvectors

Chapter 2: Dimensionality Reduction Motivation

Lecture 1: Feature Selection, Feature Extraction, and Dimensionality Reduction

Lecture 2: The Curse of Dimensionality

Chapter 3: Principal Component Analysis (PCA)

Lecture 1: An Overview of PCA

Lecture 2: A Step-by-Step Explanation of PCA on California Estates – Example

Lecture 3: The Theory Behind PCA

Lecture 4: PCA Covariance Matrix in Jupyter – Analysis and Interpretation

Chapter 4: Linear Discriminant Analysis (LDA)

Lecture 1: Overall Mean and Class Means

Lecture 2: An Overview of LDA

Lecture 3: LDA: Calculating the Within- and Between-Class Scatter Matrices

Lecture 4: A Step-by-Step Еxplanation of LDA on a Wine Quality Dataset – Example

Lecture 5: Calculating the Within- and Between-Class Scatter Matrices

Lecture 6: Calculating Eigenvectors and Eigenvalues for the LDA

Lecture 7: Analysis of LDA

Lecture 8: LDA vs. PCA

Lecture 9: Setting Up the Classifier to Compare LDA and PCA

Lecture 10: Coding the Classifier for LDA and PCA

Lecture 11: Analysis of the Training and Testing Times for the Classifier and Its Accuracy

Instructors

365 Careers
Creating opportunities for Data Science and Finance students

Rating Distribution

1 stars: 5 votes

2 stars: 15 votes

3 stars: 77 votes

4 stars: 220 votes

5 stars: 390 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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