Linear algebra provides the foundation for describing computational systems and tackling numerical problems. It is a robust and systematic framework used to represent, optimize, and solve linear systems, forming the backbone of machine learning, optimization, and data science workflows.

In this course, you will cover essential topics such as matrix operations, fundamental subspaces, projections, and singular value decomposition (SVD). You'll discover how to apply these tools to represent linear systems mathematically and computationally. By the end of the course, you'll have the experience to use linear algebra techniques confidently in real-world applications requiring precision and optimization.

 

How It Works

Course Length
2 weeks

Effort
8 to 10 hours of study per week

Format
100% online, instructor-led
  • Software engineers building AI-powered applications
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  • Engineers applying computational methods to complex systems
  • Web and frontend developers integrating machine learning features
  • Computational biologists and scientific researchers modeling real-world phenomena
  • Investment managers leveraging quantitative analysis
  • Game developers optimizing physics engines and AI behaviors
  • Anyone in a technical role seeking to strengthen their mathematical foundation for AI and machine learning
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