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machine_learning_study_notes

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Studying notes of ISLR, ESL, and other Machine Learning books. Check a more user friendly version on my personal website https://nancyyanyu.github.io/.

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# Machine Learning Study Notes
This repo is my studying notes of [An Introduction to Statistical Learning: With Applications in R](https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370), [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=sr_1_2?crid=6E2ICO74NXDY&dchild=1&keywords=elements+of+statistical+learning&qid=1599866913&s=books&sprefix=elements+of+sta%2Cstripbooks%2C191&sr=1-2), and other Machine Learning books. Check a more user friendly version on my personal website [here](https://nancyyanyu.github.io/categories/Machine-Learning/).

## Contents

- [Data Processing](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/0.Data_Processing/Data_Processing.ipynb)

- Linear Regression
  - [Linear Regression Models](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/1.Linear%20Regression/ML-Linear_Regression_Models/ML-Linear_Regression_Models.ipynb)
    > Simple Linear Regression Models; Multiple Linear Regression; Comparison of Linear Regression with K-Nearest Neighbors
  - [Potential Problems](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/1.Linear%20Regression/ML-Potential_Problems/ML-Potential_Problems.ipynb)
  - [Example: Prostate Cancer](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/1.Linear%20Regression/ML-Example-Prostate%20Cancer/ML-Example-Prostate%20Cancer.ipynb)

- Classification
  - [Logistic Regression](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/2.Classification/ML-Logistic_Regression/ML-Logistic_Regression.ipynb)
    > The Logistic Model; Estimating the Regression Coefficients; Multiple Logistic Regression
  - [Linear_Discriminant_Analysis](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/2.Classification/ML-Linear_Discriminant_Analysis/ML-Linear_Discriminant_Analysis.ipynb)
    > Using Bayes’ Theorem for Classification; Linear Discriminant Analysis for p >1; Quadratic Discriminant Analysis
    
  - [Comparing Logistic Regression, LDA, QDA, and KNN](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/2.Classification/ML-Comparing%20Logistic%20Regression%2C%20LDA%2C%20QDA%2C%20and%20KNN/ML-Comparing%20Logistic%20Regression%2C%20LDA%2C%20QDA%2C%20and%20KNN.ipynb)
  
- Resampling Methods
  - [Cross Validation, Bootstrap](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/4.Resampling_Methods/ML-Resampling%20Methods%20-%20Cross%20Validation%2C%20Bootstrap/ML-Resampling%20Methods%20-%20Cross%20Validation%2C%20Bootstrap.ipynb)
  
- Model Selection and Assessment
  - [Assessing Model Accuracy](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/3.Model_Assessment/ML-Assessing%20Model%20Accuracy/ML-Assessing%20Model%20Accuracy.ipynb)
    > Measuring the Quality of Fit; The Bias-Variance Trade-Off; Classification Setting
  - [Model Selection and Regularization](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/3.Model_Assessment/ML-Model%20Selection/ML-Model%20Selection.ipynb)
    > Subset Selection; Shrinage Methods; Considerations In High Dimensions
  - [Bias, Variance and Model Complexity](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/3.Model_Assessment/ML-Bias%2C%20Variance/ML-Bias%2C%20Variance.ipynb)
  
- Tree-Based Methods
  - [Decision Trees, Random Forest, and Boosting](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/6.Tree_Based_Models/ML-Decision%20Trees%2C%20Random%20Forest%2C%20and%20Boosting/ML-Decision%20Trees%2C%20Random%20Forest%2C%20and%20Boosting.ipynb)
    > Descision Tree; Bagging; Random Forest; Boosting
    
- [SVM](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/7.SVM/ML-SVM/ML-SVM.ipynb)
    > Maximal Margin Classifier; Support Vector Classifiers; Support Vector Machines
    
- Unsupervised Learning
  - [Clustering](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/8.Unsupervised_Learning/ML-Clustering/ML-Clustering.ipynb)
    > Clustering Methods; Hierarchical Clustering; Practical Issues in Clustering
  - [Dimension Reduction - PCA, PCR](https://github.com/nancyyanyu/machine_learning_study_notes/blob/master/8.Unsupervised_Learning/ML-Dimension_Reduction-PCA/ML-Dimension_Reduction-PCA.ipynb)
    > Dimension Reduction Methods; Principal Components Regression
    
## Reference:
* James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.

* Hastie, Trevor, et al. "The elements of statistical learning: data mining, inference and prediction." The Mathematical Intelligencer 27.2 (2005): 83-85

* Rice, John A. Mathematical statistics and data analysis. Cengage Learning, 2006.

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