Linear Algebra for Machine Learning
What you'll learn
- The applications to Machine Learning
- The Fundamentals of Linear Algebra
- Operations on a single matrices and multiple matrices
- How to perform elementary row operations
- Learn how to find the inverse Matrix
- Learn how to solve systems of linear equations
- Understand matrices as vectors and vector spaces
- Will also study Linear combinations and span
- As well as subspaces, null-space, basis, standard basis and more
- Familiarity with secondary-school-level mathematics.
- Ability to perform basic mathematical operations on numbers and fractions.
- Knowledge of how to solve linear equations.
- Understanding of basic algebra concepts.
Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding.
The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist. If we look at our original analogy, this would be akin to planes that truly fly themselves.
We are not there yet, but in this scenario the pilot becomes expensive and obsolete. However, the one person who is never obsolete is the engineer who designs the plane or the mechanic who fixes the plane. Linear Algebra is a cornerstone of machine learning. Linear Algebra not only helps improve an intuitive understanding of Machine learning. But Linear Algebra can help the machine learning engineer build better Machine Learning algorithms from Scratch or customize the parameters involved to optimize the algorithms. In this course you will learn about the Linear Algebra behind the Machine Learning Algorithm.
Who this course is for:
- Students of Machine Learning
- Students of Data Science
- Students of Statistical Learning
- Students of Linear Algebra
- Students of Mathematics
Frank holds a Doctorate in Mathematics and is currently an MBA student with a focus on computing and data science. He loves to teach and help others in any way he can. He has taught online for 15 years and in-person for 19 years at both the secondary and university level.
Besides mathematics, he is interested in education at every level and researching data science, machine learning, fuzzy logic, and mathematical education.
In his free time, Frank likes to read, play piano, play chess, spend time with his family and watch movies.