Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful.
Linear Algebra for Machine Learning and Data Science
Linear Algebra for Machine Learning and Data Science
This course is part of Mathematics for Machine Learning and Data Science Specialization
Instructor: Luis Serrano
Sponsored by Google DeepLearning AI
139,156 already enrolled
Gain insight into a topic and learn the fundamentals.
4.6
(1,775 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 34 hours
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems
Details to know
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Assessments
9 assignments
Taught in English
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This course is part of the Mathematics for Machine Learning and Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Matrices are commonly used in machine learning and data science to represent data and its transformations. In this week, you will learn how matrices naturally arise from systems of equations and how certain matrix properties can be thought in terms of operations on system of equations.
What's included
14 videos8 readings3 assignments1 app item2 ungraded labs
In this week, you will learn how to solve a system of linear equations using the elimination method and the row echelon form. You will also learn about an important property of a matrix: the rank. The concept of the rank of a matrix is useful in computer vision for compressing images.
What's included
12 videos5 readings2 assignments1 programming assignment1 ungraded lab
An individual instance (observation) of data is typically represented as a vector in machine learning. In this week, you will learn about properties and operations of vectors. You will also learn about linear transformations, matrix inverse, and one of the most important operations on matrices: the matrix multiplication. You will see how matrix multiplication naturally arises from composition of linear transformations. Finally, you will learn how to apply some of the properties of matrices and vectors that you have learned so far to neural networks.
What's included
14 videos3 readings2 assignments1 programming assignment3 ungraded labs
In this final week, you will take a deeper look at determinants. You will learn how determinants can be geometrically interpreted as an area and how to calculate determinant of product and inverse of matrices. We conclude this course with eigenvalues and eigenvectors. Eigenvectors are used in dimensionality reduction in machine learning. You will see how eigenvectors naturally follow from the concept of eigenbases.
What's included
20 videos7 readings2 assignments1 programming assignment1 ungraded lab
Instructor
Instructor ratings
4.7 (602 ratings)Offered by
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PA
5
Reviewed on May 27, 2023
Best Visual Explanation, I've got new thinking of the same things which I had learned in the Past. It great Course Thanks for making Such Amazing Content.
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5
Reviewed on Nov 1, 2024
The course was awesome! I finally got to understand how neural networks work, I had no idea calculus would help create these! The course was very clear, specially if you know calculus.
SP
5
Reviewed on Jul 26, 2023
This course is truly exceptional for individuals eager to strengthen their grasp of Linear Algebra concepts, paving the way for a deeper understanding of machine learning and data science.
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