/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

Primary LanguagePythonMIT LicenseMIT

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 (rmaestre@gmail.com or roberto.maestre@campus.eae.es)