A look at fundamental Machine Learning algorithms implemented in Python at 3 levels:
_simple.pywith as few imports, abstractions, and mutations as possible. No classes. Reinventing the wheel... to a degree._oop.pyOOP approach, more common libraries used._sklearn/pytorch.pyusing Frameworks and modern libraries.
We need to walk before we run. Nowadays a lot of ML Engineers via YT, bootcamps or other mediums, learn how to use the 'latest buzz word' Framework/API in ML, particularly in NLP. However, as always, there is a huge disconnect between tech recruiters, senior devs, and upper management when it comes to finding a good candidate. Jumping from trendy library to another creates a new level of developers that lack deep knowledge.
I aim to demonstarte my knowledge behind these fundamental algorithms from scratch. From there, we can abstract it away to any level via any library or framework, down to 5 lines of code. But knowing whats under the hood, and able to hack away to problem solve, as plug-and-play doesn't always work for ever situation, is the corner-stone of a valuable developer.
Jupyter notebooks are provided to break down code into smaller parts with explanations.