Powered by
Completion
CERTIFICATE
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
Course Cost
Free course
Intermediate
Skill Level
32 Hours
Self-paced lessons
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.
1,24,290 Enrolled
English
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
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
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
Reviews
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
4.9
205
Reviews
1,54,156
Students
4
Courses
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.