Hello again, everyone! Thank you for your interest and feedback on the first part of my Machine Learning Toolkit. As promised, I'm back with Part II. Just like the first part, this is not just a list of algorithms. It's a quick guide that includes ideal use-cases, real-world examples (although they might be basic without code for now), a look into the math behind each algorithm, and its limitations.

Quick Recap and What’s Next>

In Part I, we covered foundational algorithms such as Linear Regression, Logistic Regression, Decision Trees, SVMs, and k-NN. If you missed it, you can check it out here.

For this second part, we'll discuss:

Like in Part I, I've added some AI-generated images to make the notebook more interesting. This time, they are from OpenAI's Dalle-3. I've also included educational YouTube videos that I find helpful for each topic.

If you are interested in generative AI images, feel free to follow this account where I post daily new content and the prompts used generating images. And for those who are looking forward to the code and real-world examples, that part is also in progress.

If these topics interest you, here is the link to the notebook for Part II. For the best experience, it's good to download and run it locally. As always, your feedback and questions are welcome.

Thank you for joining me. Stay tuned for Part III, where we will discuss Neural Networks.