✔️ ML Algorithms * by Jason Brownlee

👏 최소한에라도 공부하고 알아야 할 machine learning algorithm 목록들이다.

(훗날 아래 모든 algorithm을 포스팅할 것!)

 

 

🟠 Regularization

 

→ Ridge Regression

https://sh-avid-learner.tistory.com/entry/L2-Regularization-%E2%86%92-Ridge-Regression-concepts

https://sh-avid-learner.tistory.com/entry/L2-Regularization-%E2%86%92-Ridge-Regression-wscikit-learn

 

→ LASSO (Least Absolute Shrinkage and Selection Operator)

https://sh-avid-learner.tistory.com/141

 

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🔵 Regression

 

→ Linear Regression (Simple Linear Regression, Multiple Linear Regression)

https://sh-avid-learner.tistory.com/entry/Simple-Linear-Regression-concepts

https://sh-avid-learner.tistory.com/entry/Simple-Linear-Regression-Model-wscikit-learn

https://sh-avid-learner.tistory.com/entry/Multiple-Linear-Regression-Model-conceptswcode

 

→ Polynomial Regression

https://sh-avid-learner.tistory.com/entry/Polynomial-Regression-Model

 

→ Logistic Regression

https://sh-avid-learner.tistory.com/entry/Logistic-Regression-Model-concepts

https://sh-avid-learner.tistory.com/entry/Logistic-Regression-Model-wcode

 

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🟣 Bayesian

 

→ Bayesian Theorem

https://sh-avid-learner.tistory.com/entry/Bayesian-Theorem

https://sh-avid-learner.tistory.com/entry/Bayesian-Theorem-example-2-exercises

 

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⚪️ Decision Tree

 

→ concepts

https://sh-avid-learner.tistory.com/entry/Decision-Trees-concepts

 

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🟤 Dimensionality Reduction

 

→ PCA(Principal Component Analysis)

https://sh-avid-learner.tistory.com/entry/feature-extraction1-PCAPrincipal-Component-Analysis-concepts

https://sh-avid-learner.tistory.com/entry/feature-extraction1-PCAPrincipal-Component-Analysis-wcode

 

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🟡 Clustering

 

→ K-Means

https://sh-avid-learner.tistory.com/entry/K-Means-Clustering-concepts-wcode