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.
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
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
Skills you'll gain
Details to know
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Recently updated!
July 2025
Assessments
7 assignments
Taught in English
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There are 2 modules in this course
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
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
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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.
More questions
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