DeepLearning.AI - Linear Algebra for Machine Learning and Data Science
- Offered byCoursera
Linear Algebra for Machine Learning and Data Science at Coursera Overview
Duration | 21 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Linear Algebra for Machine Learning and Data Science at Coursera Highlights
- Earn a Certificate upon completion
Linear Algebra for Machine Learning and Data Science at Coursera Course details
- Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano
- This beginner-friendly program is where you?ll master the fundamental mathematics toolkit of machine learning
- This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works
- Upon completion, you?ll understand the mathematics behind all the most common algorithms and data analysis techniques ? plus the know-how to incorporate them into your machine learning career
Linear Algebra for Machine Learning and Data Science at Coursera Curriculum
Week 1: System of linear equations
Specialization introduction
Course introduction
What to expect and how to succeed
Machine learning motivation
System of sentences
System of equations
System of equations as lines
A geometric notion of singularity
Singular vs nonsingular matrices
Linear dependence and independence
The determinant
System of equations (3x3)
Singular vs non-singular matrices (3x3)
System of equations as planes (3x3)
Linear dependence and independence (3x3)
The determinant (3x3)
Conclusion
(Optional) Downloading your Notebook and Refreshing your Workspace
Solving systems of linear equations
Matrices
Week 2: Solving system of linear equations
Machine learning motivation
Solving non-singular systems of linear equations
Solving singular systems of linear equations
Solving systems of equations with more variables
Matrix row-reduction
Row operations that preserve singularity
The rank of a matrix
The rank of a matrix in general
Row echelon form
Row echelon form in general
Reduced row echelon form
Conclusion
(Optional) Assignment Troubleshooting Tips
(Optional) Partial Grading for Assignments
Method of Elimination
The Rank of a matrix
Week 3: Vectors and Linear Transformations
Machine Learning Motivation
Vectors and their properties
Sum and difference of vectors
Distance between vectors
Multiplying a vector by a scalar
The dot product
Geometric Dot Product
Multiplying a matrix by a vector
Matrices as linear transformations
Linear transformations as matrices
Matrix multiplication
The identity matrix
Matrix inverse
Which matrices have an inverse?
Neural networks and matrices
Conclusion
Vector operations: Sum, difference, multiplication, dot product
Vector and Matrix Operations, Types of Matrices
Week 4: Determinants and Eigenvectors
Machine learning motivation
Singularity and rank of linear transformations
Determinant as an area
Determinant of a product
Determinants of inverses
Bases in Linear Algebra
Span in Linear Algebra
Eigenbases
Eigenvalues and eigenvectors
Conclusion
Reading: Textbooks and resources
References
Notations
Acknowledgments
Determinants and Linear Transformations
Eigenvalues and Eigenvectors
Linear Algebra for Machine Learning and Data Science at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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