Hey everyone! I'm a second-year Computer Science student who's really passionate about getting into Machine Learning (ML). I've always been fascinated by the idea of creating algorithms that can handle complex tasks, especially those that adapt to different variables. I've been digging into ML on my own, and I realize it involves a solid grasp of math and data structures, but I'm eager to get started! Currently, I'm learning C to better understand low-level computing before I dive into ML frameworks and libraries. My main programming language is Python, and I know about scikit-learn, but I really want to build my first model from scratch to truly grasp how it works. I've been learning OOP in Python and pointers in C, and I want to make the most of my time this holiday by building a strong foundation. So, can anyone suggest books, websites, or any resources that focus on the fundamentals I need for my first ML model, while also helping me grow as a software engineer?
2 Answers
I highly recommend checking out the 'Math for ML' course along with the ML specialization by DeepLearning.AI on Coursera. While it uses Python and dives into libraries, it's very informative. Also, don't miss the free courses available on YouTube - the FreeCodeCamp channel has some great material!
You should definitely look at the YouTube series 'Neural Networks / Deep Learning' on StatQuest. It’s got a ton of helpful info and the explanations are super clear. It's helped a lot of people, including me! Check it out here: youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1
This playlist looks fantastic, thanks! I love that the guy has a fun style too!
Thanks for the suggestions! I’m really looking to understand the fundamentals before jumping into libraries, but I know those will be important too.