What you will learn
- Understand how to represent data as vectors and matrices, and identify their properties using concepts like singularity, rank, and linear independence.
- Apply common vector and matrix algebra operations such as dot product, inverse, and determinants.
Express certain types of matrix operations as linear transformations, and apply concepts of eigenvalues and eigenvectors to machine learning problems.
Program Overview
Systems of Linear Equations
⏱️ 8 hours
- Learn how matrices arise from systems of equations and how certain matrix properties can be understood in terms of operations on systems of equations.
- Explore concepts like singularity, linear dependence and independence, and determinants.
Vector and Matrix Operations
⏱️ 8 hours
- Dive into vector operations, including sum, difference, multiplication, and dot product.GitHub
- Understand different types of matrices and their operations.
Linear Transformations
⏱️9 hours
- Study linear transformations and how they can be represented using matrices.
- Apply these concepts to machine learning problems.
Eigenvalues and Eigenvectors
⏱️9 hours
- Learn about eigenvalues and eigenvectors and their significance in machine learning.
- Apply these concepts to problems like Principal Component Analysis (PCA).
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Job Outlook
- A solid understanding of linear algebra is crucial for careers in machine learning and data science.
- Proficiency in these concepts enhances one’s ability to develop and optimize machine learning models.
- Employers value candidates who can bridge the gap between theoretical concepts and practical implementation in data-driven roles.
Specification: Linear Algebra for Machine Learning and Data Science
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