Linear Algebra for ML and Analytics Training
By External Provider
About 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.
What you will 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