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URL: https://www.coursera.org/learn/linear-algebra-for-ml-and-analytics-training

⇱ Linear Algebra for ML and Analytics Training | Coursera

Linear Algebra for ML and Analytics Training

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Linear Algebra for ML and Analytics Training

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the role of linear equations in data science and distinguish between linear and non-linear forms

  • Perform matrix operations and use vectors for linear transformations in machine learning

  • Apply concepts like systems of equations and matrix types to solve real-world problems

  • Build a strong foundation in linear algebra for machine learning, data analysis, and PCA applications

Details to know

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Assessments

7 assignments

Taught in English

There are 2 modules in this course

This beginner-friendly course covers core linear algebra concepts essential for data science and machine learning. Start with linear equations and learn to identify linear vs. non-linear forms and solve systems with real-world examples. Then explore matrices and vectors, including matrix operations, special matrix types, and vector roles in linear transformations. Finally, discover how these foundations support techniques like Principal Component Analysis (PCA) for dimensionality reduction and data analysis.

To be successful in this course, no prior experience is required. It’s ideal for students, aspiring data scientists, and machine learning beginners looking to strengthen their math foundation. By the end of this course, you will be able to: - Understand and apply linear equations and their forms - Identify and solve systems of linear equations - Perform matrix operations and work with special matrices - Use vectors in linear transformations - Apply linear algebra concepts in PCA and machine learning workflows Ideal for future data analysts, ML engineers, and AI professionals.

Explore the foundations of linear algebra in this beginner-friendly course. Understand the core concepts of linear equations and how they apply to data science and machine learning. Learn to identify linear vs. non-linear equations, explore different forms of linear equations, and solve systems of equations with real-world examples. Ideal for learners building a strong base in analytical thinking and mathematical modeling.

What's included

9 videos1 reading4 assignments

9 videos•Total 15 minutes
  • Introduction•0 minutes
  • Introduction to Linear Algebra•0 minutes
  • Linear Equations•1 minute
  • Linear Equations - Example•1 minute
  • Identifying Linear and Non-linear Equations•1 minute
  • Forms of Linear Equation•3 minutes
  • Linear Equation Form Example•1 minute
  • System of Linear Equations•1 minute
  • Solving Linear Systems of Equation•5 minutes
1 reading•Total 10 minutes
  • Course Syllabus•10 minutes
4 assignments•Total 85 minutes
  • Quiz on Introduction to Linear Equations•15 minutes
  • Quiz on Understanding Linear Equations•15 minutes
  • Quiz on Systems of Linear Equations•15 minutes
  • Assessment for Fundamentals of Linear Equations•40 minutes

Explore the core concepts of matrices and vectors in this hands-on course. Learn the basics of matrix operations, explore different matrix types, and understand their applications in data science. Get introduced to vectors and their role in linear transformations. Discover how matrices and vectors power techniques like Principal Component Analysis (PCA). Ideal for learners building strong foundations in machine learning math.

What's included

5 videos3 assignments

5 videos•Total 15 minutes
  • Introduction to Matrices•2 minutes
  • Matrix Operations•6 minutes
  • Special Matrix Types•1 minute
  • Introduction to Vectors•1 minute
  • Principal Component Analysis (PCA)•4 minutes
3 assignments•Total 70 minutes
  • Quiz on Basics of Matrices•15 minutes
  • Quiz on Types and Applications•15 minutes
  • Assessment for Matrices and Vectors•40 minutes

Instructor

Simplilearn
87 Courses•77,755 learners

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Frequently asked questions

Yes, linear algebra is fundamental to machine learning as it underpins models, algorithms, and data representations.

Absolutely. It helps structure, transform, and analyze large datasets efficiently, especially in statistical and predictive modeling.

You can start with beginner-friendly online courses focused on linear algebra for data science and ML, such as those offered by SkillUp, Coursera, or Khan Academy.

Linear algebra, calculus, probability, and statistics are the most essential, with linear algebra forming the backbone of most ML algorithms.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,