29 lines
950 B
Markdown
29 lines
950 B
Markdown
# Machine Learning Notes
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Welcome to your simplified Machine Learning notes! These notes are designed to be easy to understand.
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## Table of Contents
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1. [[01_Introduction_to_ML|Introduction to Machine Learning]]
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- Supervised Learning
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- Regression vs Classification
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2. [[02_Data_Science_Process|Standard Process for Data Science (CRISP-DM)]]
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- The 6 phases of a project
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3. [[03_Logistic_Regression|Logistic Regression]]
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- Odds and Probability
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- Sigmoid Function
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4. [[04_Model_Evaluation|Model Evaluation Metrics]]
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- Confusion Matrix, Accuracy, Precision, Recall
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- ROC and AUC
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5. [[05_Imbalanced_Data|Handling Imbalanced Data]]
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- SMOTE and Resampling
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6. [[06_KNN_Algorithm|K-Nearest Neighbors (KNN)]]
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- Distance Measures
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- How KNN works
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7. [[07_Naive_Bayes|Naive Bayes Classifier]]
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- Bayes Theorem
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- Spam Filter Example
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8. [[08_Decision_Tree|Decision Tree Algorithm]]
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- Nodes and Splitting
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- Gini and Entropy
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