Lecture Notes

Source folder: Lecture-Notes/

Summary

Notebook collection used throughout the course for in-class demonstrations and concept practice.

Analysis Used

  • Introductory classification work on Iris (k-NN, train/test splits, confusion matrices).
  • Data preprocessing and PCA examples (standardization, normalization, correlation analysis).
  • Gradient-descent derivations and optimization walkthroughs.
  • Clustering and missing-data demos (KMeans, nullity visual checks).
  • Later-topic overviews including decision trees, cross-validation, SVMs, and neural-network basics.

Technologies and Methods

  • Python, Jupyter Notebook
  • pandas, numpy, matplotlib, plotly
  • scikit-learn (classification, clustering, preprocessing)
  • missingno
  • Supporting CSV data for exercises (country-data.csv, world-data-2023.csv)