About this Course
One of the most interesting topics to visualize in Linear Algebra are Eigenvectors and Eigenvalues. Here you will learn how to easily calculate them and how they are applicable and particularly interesting when it comes to machine learning implementations.
Course Cost
Free
Timeline
Approx. 1 week
Skill Level
Beginner
Included in Course
Rich Learning Content
Interactive Quizzes
Taught by Industry Pros
Self-Paced Learning
Student Support Community
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This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.
Free Course
Eigenvectors and Eigenvalues
Enhance your skill set and boost your hirability through innovative, independent learning.
Course Leads
-
Ortal Arel
Instructor
What You Will Learn
Lesson 1
Vectors
- Linear Transformation
Lesson 1
Vectors
- Linear Transformation
Lesson 2
Definitions and Calculations
- Characteristic Equation of a matrix
- Eigenvalues
- Eigenvectors
Lesson 2
Definitions and Calculations
- Characteristic Equation of a matrix
- Eigenvalues
- Eigenvectors
Lesson 3
Why is the relevant to Machine Learning?
- Principle Component Analysis (PCA)
Lesson 3
Why is the relevant to Machine Learning?
- Principle Component Analysis (PCA)
Prerequisites and Requirements
To easily understand this class you will need to have mathematical background in Linear Algebra. Refresh your memory or go over the topics of Linear transformation , Determinants And a System of linear equations before beginning.
See the Technology Requirements for using Udacity.
Why Take This Course
In the computational world of AI you will often encounter enormous amounts of data that needs to be processed. Often, the data volume will be so large that you will need to use some form of data reduction technique. Eigen-concepts are a big part of the mathematical background needed to understand a useful data reduction tools, called Principal Component Analysis (PCA).
What do I get?
- Instructor videos
- Learn by doing exercises
- Taught by industry professionals
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