unit 2 added

unit 2/00_Index.md

@@ -0,0 +1,28 @@
+# 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