# 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|Introduction to Machine Learning]]
   - Supervised Learning
   - Regression vs Classification
2. [[02_Data_Science_Process|Standard Process for Data Science (CRISP-DM)]]
   - The 6 phases of a project
3. [[03_Logistic_Regression|Logistic Regression]]
   - Odds and Probability
   - Sigmoid Function
4. [[04_Model_Evaluation|Model Evaluation Metrics]]
   - Confusion Matrix, Accuracy, Precision, Recall
   - ROC and AUC
5. [[05_Imbalanced_Data|Handling Imbalanced Data]]
   - SMOTE and Resampling
6. [[06_KNN_Algorithm|K-Nearest Neighbors (KNN)]]
   - Distance Measures
   - How KNN works
7. [[07_Naive_Bayes|Naive Bayes Classifier]]
   - Bayes Theorem
   - Spam Filter Example
8. [[08_Decision_Tree|Decision Tree Algorithm]]
   - Nodes and Splitting
   - Gini and Entropy