First Steps in Linear Algebra for Machine Learning
- 4.1
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Approx. 21 hours to complete
Course Summary
This course covers the basic concepts of linear algebra, an essential mathematical tool for machine learning. It provides a foundation for understanding advanced machine learning techniques.Key Learning Points
- Learn the fundamental concepts of linear algebra for machine learning
- Understand the role of linear algebra in solving real-world machine learning problems
- Apply linear algebra to create machine learning models
Related Topics for further study
Learning Outcomes
- Understand the fundamental concepts of linear algebra
- Apply linear algebra to solve real-world machine learning problems
- Create machine learning models using linear algebra
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of calculus and statistics
- Familiarity with programming in Python
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Linear Algebra for Beginners: Open Doors to Great Careers
- Linear Algebra - Foundations to Frontiers
Related Education Paths
Notable People in This Field
- Founder, Coursera
- Chief AI Scientist, Facebook
Related Books
Description
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability.
Outline
- Systems of linear equations and linear classifier
- About the University
- Introduction to Linear Algebra
- Linear Algebra and Calculus
- Matrices and Multidimensional Vectors
- Matrix arithmetics
- Properties of matrix operations and some special matrices
- Vectors and matrices in Python
- Systems of linear equations
- Matrix inverse
- Gaussian elimination. The first example
- Elementary row operations
- Gaussian elimination. Main theorem.
- Gaussian Elimination. The algorithm.
- The Inverse matrix with Gaussian elimination
- LU and PLU decomposition
- About University
- Rules on the academic integrity in the course
- Covered Python methods
- Matrices and multidimensional vectors
- Matrix arithmetics
- Systems of linear equations
- Matrix inverse
- Gaussian elimination. Main theorem
- The Inverse matrix with Gaussian elimination
- LU and PLU decomposition
- Week 1
- Full rank decomposition and systems of linear equations
- Overview of the second week
- Abstract algebra and linear algebra
- Axioms of vector spaces: first application
- Examples of vector spaces
- Subspaces
- Linear combinations and spans
- Basis and linear dependence
- Dimension of a vector space
- Examples of bases
- Linear dependence and rank
- Formula for the solution of a SLAE
- An example of vector representation of the set of solutions
- Rouché–Capelli Theorem
- Full rank decomposition
- Linear dependence and rank
- An example of vector representation of the set of solutions
- Rouché–Capelli Theorem
- Full rank decomposition
- Week 2
- Euclidean spaces
- Coordinates and basis
- Coordinates change example
- Euclidean space
- Geometry and Euclidean spaces
- Orthogonal and orthonormal bases
- Distance and orthogonal projections
- Inconsistent systems and the least squares method
- Linear regression example
- Introduction to support vector machine
- Linear regression and SVM with Python
- Coordinates and basis
- Coordinates change example
- Euclidian space
- Distance and orthogonal projections
- Linear regression example
- Week 3
- Final Project
- Conclusion and intro to project
- References and further reading
- Life expectancy prediction quiz
Summary of User Reviews
The First Steps in Linear Algebra course is highly recommended for anyone interested in machine learning. Users have praised the course for its clear explanations and practical exercises.Key Aspect Users Liked About This Course
Clear explanations and practical exercisesPros from User Reviews
- Great introduction to linear algebra for machine learning
- The course is well-structured and easy to follow
- The instructor explains complex concepts in a simple manner
- The hands-on exercises are very helpful for understanding the material
- The course provides a good foundation for further study in machine learning
Cons from User Reviews
- Some of the exercises are too easy and don't challenge the user enough
- The course can be a bit slow-paced for experienced users
- Some of the explanations are too simplistic and don't provide enough detail
- The course doesn't cover advanced topics in linear algebra for machine learning
- The video lectures can be a bit dry and monotonous
Dmitri Piontkovski
4.1 Raiting
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