DMCT-NOTES/unit 2/00_Index.md

Machine Learning Notes

Welcome to your simplified Machine Learning notes! These notes are designed to be easy to understand.

Table of Contents

1. 01_Introduction_to_ML
   • Supervised Learning
   • Regression vs Classification
2. 02_Data_Science_Process
   • The 6 phases of a project
3. 03_Logistic_Regression
   • Odds and Probability
   • Sigmoid Function
4. 04_Model_Evaluation
   • Confusion Matrix, Accuracy, Precision, Recall
   • ROC and AUC
5. 05_Imbalanced_Data
   • SMOTE and Resampling
6. 06_KNN_Algorithm
   • Distance Measures
   • How KNN works
7. 07_Naive_Bayes
   • Bayes Theorem
   • Spam Filter Example
8. 08_Decision_Tree
   • Nodes and Splitting
   • Gini and Entropy