rmaestre / ml-code-lectures

This repo has been implemented from scratch and serves as base to the PhD and MSc courses that I have imparted on Business and Data Science

ML Code lectures

This repo has been implemented from scratch and serves as base to the PhD and MSc courses that I have imparted on Bussines and Data Science. The final goal is to provide a low-level details for classic ML models and also providing good programming concepts such OOP, recursion and Big O notions.

Theory, notebooks with explanations, bussines concepts and practices are not included here.

Models (WIP)

• Kmeans
• Neuron (classification)
• Decision Tree (clasification and regression)
• Random Forest (with feature importance)
• MLP (nnfs) (Cross-entropy loss function)
• Isolation Forest
• • Knn
• • Lineal regression
• • General Additive Models
• • Multinomial Naive Bayes

Feedback

All feedback and bug reporting are welcomed ([e-mail] or [e-mail])