Foundations for Machine Learning

Currently Playing: Foundations for Machine Learning | Linear Algebra | Vector, Transformation, Span, Basis [Lecture 2]

Linear Algebra for ML: Start of our new course on YouTube Linear Algebra is one of the foundational pillars of ML. But how exactly? When you multiply a vector (read list of features) by a matrix (read weight matrix), you are simply performing linear transformations. Sometimes this transformation takes the vector from a lower dimensional space to a higher dimension or vice versa. Consider the following cases. 1. You may have heard of or studied span and basis and wondered where exactly is this useful. 2. You might have found linear algebra very difficult in the beginning. I certainly did. 3. You wish to learn ML from its mathematical foundations but never got a chance to do so. 4. You know how to run Google Colab notebooks. However, you are not confident about your ML skills because you lack foundational knowledge. If you have experienced any of the above here is a resource for you. For the past 4 months, I have been working on a course to lay the foundations for ML. I have released this course on Vizuara's YouTube channel titled "Foundations for Machine Learning." This will be a 45-hour course with ~65 lectures. Here is the first lecture that introduces Linear Algebra with an emphasis on its utility for ML. Check this out. This is the starting lecture. I am sure you are going to enjoy: https://lnkd.in/gg__8R47 There are no prerequisites. If you have basic logical thinking capability and a willingness to dedicate time, consistently, you can follow this course. I have tried to simplify the course content as much as possible by trying to give you a logical and geometric intuition rather than only mathematical steps.