Linear Algebra for Machine Learning and Data Science
Master essential linear algebra concepts for machine learning with hands-on Python implementation.Linear Algebra for Machine Learning and Data Science
Master essential linear algebra concepts for machine learning with hands-on Python implementation.Linear Algebra for Machine Learning and Data Science
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mathematics for Machine Learning and Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.5
(1,670 ratings)
1,24,290 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Master vector and matrix operations with practical applications
Understand linear transformations and their role in ML
Apply eigenvalues and eigenvectors to ML problems
Implement linear algebra concepts using Python
Develop mathematical foundations for advanced ML concepts
Skills you'll gain
This course includes:
4.65 Hours PreRecorded video
8 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers fundamental linear algebra concepts essential for machine learning and data science. Students learn to represent data using vectors and matrices, perform matrix operations, understand linear transformations, and apply eigenvalues and eigenvectors to machine learning problems. The curriculum integrates theoretical concepts with practical Python programming exercises, preparing learners for real-world applications in machine learning.
Week 1: Systems of linear equations
Module 1 · 8 Hours to complete
Week 2: Solving systems of linear equations
Module 2 · 8 Hours to complete
Week 3: Vectors and Linear Transformations
Module 3 · 9 Hours to complete
Week 4: Determinants and Eigenvectors
Module 4 · 7 Hours to complete
Fee Structure
Instructor
Quantum AI Research Scientist and Educator
Luis Serrano is an accomplished AI scientist, popular YouTuber, and author of the book "Grokking Machine Learning." Currently, he serves as a quantum AI research scientist at Zapata Computing in Toronto, where he develops machine learning algorithms for quantum computers. Previously, he held significant roles in Silicon Valley, including lead AI educator at Apple, head of content for AI and Data Science at Udacity, and a member of the video recommendations team at Google’s YouTube. With a strong academic background, including a Bachelor's and Master's from the University of Waterloo and a PhD from the University of Michigan, Luis has a deep passion for mathematics that began in his youth when he represented Colombia in the International Mathematical Olympiads. Through his work and online presence, he aims to make complex AI concepts accessible to a broader audience while continuing to contribute to the advancement of quantum computing.
Testimonials
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4.5 course rating
1,670 ratings
Frequently asked questions
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