Hello, everyone! I think that one of the best ways to truly learn something is to try to explain it to others. With that in mind, I've been working on a Machine Learning toolkit. This isn't just a collection of algorithms; it's a quick guide that includes the ideal use-case, a real-world example, a quick look at the math behind each algorithm, and its limitations.

Designed to refresh my own knowledge, this toolkit also serves as a resource for anyone interested in Machine Learning. I hope you find it useful too.

This toolkit will be as a trilogy of notebooks:

Part I: Covering foundational algorithms like Linear Regression, Logistic Regression, Decision Trees, SVMS, k-NN.(Available)

Part II: Will cover Gradient Boosting, Ridge Regression, Lasso Regression, K-Means Clustering, Hierarchical Clustering, Principal Component Analysis, ARIMA, AdaBoost, and Bagging. (In Progress)

Part III: Will be an introduction to Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. (In Progress)

Besides the technical stuff, I've spiced it up with some fun AI-generated images from Leonardo.ai using Dreamshaper v7 and Leonardo diffusion models. They might not explain the algorithms, but they definitely add a splash of creativity to how the models thought those concepts should be presented in an abstract way.

I've also added StatsQuest's YouTube videos under each section. These videos are a few years old, but I still find them really helpful for these topics.

The notebook serves a dual purpose—to refresh my own understanding and to share this with you. I've tried to keep the explanations simple and clear, making it accessible for learners at any stage.

I've recently finished Part I and wanted to share it with you all. Github link (works best if you download and run it locally). Feel free to check it out and stay tuned for Parts II and III. Your feedback is always welcome.