Students will get an opportunity to explore the complex mathematics behind machine learning algorithms.
Students will be able to write machine learning algorithms from scratch.
Students will get guidance on how to build in the knowledge they gained in the course.
Student will be receive life-long access to the course for future reference.
Requirements
- Students should be well-versed with calculus and should know basic python syntax.
Description
This course is for students who are looking for logic behind the myriad of machine learning algorithms they use every day. When I started my journey with machine learning, it was really difficult for me to intuitively understand the code I was writing. However, after watching multiple videos and reading millions of articles, I finally understood the fundamentals of machine learning algorithms. In this course, I’ll walk you through the mathematical concepts you need to know to understand and implement a machine learning algorithm. Other than that. you’ll also learn how to build the same algorithms from scratch using python. No kind of libraries will be imported during the course. This will help you in understanding the algorithm properly as none of the work will be taking place in the background. This course does not feature high-level machine algorithms instead it focuses on the most basic ones: bivariate regression, multivariate regression, support vector regression, k-nearest neighbors. The scope of this course will gradually expand and soon it will feature tutorials on techniques like deep neural networks. This course is a condensed version of my knowledge which I gained through multiple resources. You are free to drop in your queries in the Q&A section, I will be glad to resolve them. Happy coding 😉
Who this course is for:
- High-schoolers and freshmen with an urge to explore machine learning.
Course content
