Free Linear Algebra Course for Beginner level by Johns Hopkins University
Audit this course for free on Coursera!! Go and learn for free!!
Linear algebra is like the toolbox of data science. It’s important because it helps data scientists work with data more efficiently. We use it to organize data and understand how to make predictions or find patterns in the information.
When we’re building machine learning models, which are like smart tools that learn from data, we need linear algebra to make them work. It’s also crucial for simplifying complex data and making predictions. In simple terms, linear algebra is the key that helps data scientists unlock the potential hidden in data and create useful tools.
This course is provided by Johns Hopkins University on the Coursera platform which you can audit for free.
Information: Beginner level, 10 hours (approximately), Flexible schedule
Syllabus:
1. Introductions to Matrices: In this module, the focus is on linear systems and matrices. Two key questions are addressed: the existence and uniqueness of solutions. To answer these, an important concept called an “invariant” is introduced. The Row Reduction Algorithm helps determine the number of pivot positions in a matrix, which is crucial for solving linear systems. These fundamental concepts are revisited throughout the course, emphasizing the importance of understanding new terms, technical skills, and the theory behind these algorithms.
2. Vector and Matrix Equations: In this section, you briefly shift your focus from linear systems to explore the world of vectors. You’ll find that understanding linear combinations with vectors is akin to solving linear equations, highlighting the deep connections within linear algebra. You also introduce the concept of matrices as functions acting on vectors, showing how questions about matrix properties are answered by solving linear systems. These connections illustrate why linear algebra is often called the “theory of everything.”
3. Linear Transformations: In this module, you explore vectors and their functions, which have broad applications in physics, computer science, math, and data science. You learn about linear dependence, independence, and linear transformations. You’ll also discover how a linear transformation can be represented by a matrix, how to find that matrix, and how to extract information about the transformation. The definitions introduced here are crucial for the upcoming modules, so pay close attention.
This course is taught by Joseph W. Cutrone (Ph.D. in Mathematics from Johns Hopkins University).
You can audit this course here.
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