Hello there! ๐ Welcome to my repository for the DAT158 Machine Learning course at HVL. This project is as simple as it gets - a straightforward dive into some basic ML concepts. It's perfect if you're just starting out, but for ML veterans, it's a walk in the park!
In compliance with the course requirements (and a great chance to dabble in GitHub), this repository is a collection of my attempts and explorations in Machine Learning - tailored specifically for the DAT158 course. It's simple, basic, and probably not a revelation for anyone well-versed in ML or who has taken this course.
- Simple Search Algorithms: Boyer Moore, KMP, and LCS - explained and illustrated in the simplest way.
- Python Scripts: Basic Python code that barely scratches the surface of what Machine Learning has to offer.
- PDF with Basic Graphs: Some rudimentary graphs and drawings that illustrate algorithm concepts.
- Illustrations for Beginners: Simple visual aids for those just getting their feet wet in ML.
- Compulsory Submission: Yep, it's a course requirement to put this on GitHub. So here it is, in all its simplistic glory!
- Showcasing Basics: A humble display of my learning process in the ML course.
If you're looking for advanced ML insights, this might not be the stop for you. But if you're a beginner or my fellow DAT158 mate, feel free to explore and learn together!
- A shoutout to my instructors at HVL for pushing me into the open-source world.
- Gratitude to coffee, for being the true MVP during the late-night deadline.
Enjoy exploring this simple ML project. Star it if you find it helpful or amusing, and may your journey in Machine Learning be ever enlightening!