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4. Supervised Learning

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4.1 Regression
4.1 Regression
 
 
4.2 Classification
4.2 Classification
 
 
README.md
README.md
 
 

README.md

Supervised Learning

Welcome to the Supervised Learning section of our repository! This folder contains various materials and resources related to supervised learning algorithms and techniques. The goal is to provide a comprehensive understanding of how to develop, evaluate, and fine-tune models for both regression and classification tasks in machine learning and deep learning.

4. Supervised Learning

4.1 Regression

4.1.1 Linear Regression

  • 4.1.1.1 Simple Linear Regression
    • Explanation of Simple Linear Regression and its applications
    • Methods for implementing Simple Linear Regression
  • 4.1.1.2 Multiple Linear Regression
    • Explanation of Multiple Linear Regression and its benefits
    • Methods for implementing Multiple Linear Regression

4.1.2 Polynomial and Regularized Regression

  • 4.1.2.1 Polynomial Regression
    • Explanation of Polynomial Regression and scenarios where it is applicable
    • Methods for implementing Polynomial Regression
  • 4.1.2.2 Ridge and Lasso Regression
    • Explanation of Ridge and Lasso Regression, their benefits, and differences
    • Methods for implementing Ridge and Lasso Regression
  • 4.1.2.3 Elastic Net Regression
    • Explanation of Elastic Net Regression and its benefits
    • Methods for implementing Elastic Net Regression

4.1.3 Tree-Based and Neighbor-Based Regression

  • 4.1.3.1 Decision Tree Regression
    • Explanation of Decision Tree Regression and its applications
    • Methods for implementing Decision Tree Regression
  • 4.1.3.2 Random Forest Regression
    • Explanation of Random Forest Regression and its benefits
    • Methods for implementing Random Forest Regression
  • 4.1.3.3 K-Nearest Neighbors Regression (KNN)
    • Explanation of K-Nearest Neighbors Regression and its applications
    • Methods for implementing K-Nearest Neighbors Regression

4.1.4 Advanced Regression Methods

  • 4.1.4.1 Support Vector Regression (SVR)
    • Explanation of Support Vector Regression and its scenarios
    • Methods for implementing Support Vector Regression
  • 4.1.4.2 Bayesian Regression
    • Explanation of Bayesian Regression and its applications
    • Methods for implementing Bayesian Regression
  • 4.1.4.3 Locally Weighted Linear Regression (LWLR)
    • Explanation of Locally Weighted Linear Regression and its scenarios
    • Methods for implementing Locally Weighted Linear Regression

4.1.5 Dimensionality Reduction-Based Regression

  • 4.1.5.1 Principal Component Regression (PCR)
    • Explanation of Principal Component Regression and its applications
    • Methods for implementing Principal Component Regression
  • 4.1.5.2 Partial Least Squares Regression (PLS)
    • Explanation of Partial Least Squares Regression and its applications
    • Methods for implementing Partial Least Squares Regression

4.2 Classification

4.2.1 Basic Classification Models

  • 4.2.1.1 Logistic Regression
    • Explanation of Logistic Regression and its applications
    • Methods for implementing Logistic Regression
  • 4.2.1.2 K-Nearest Neighbors (KNN)
    • Explanation of K-Nearest Neighbors and its scenarios
    • Methods for implementing K-Nearest Neighbors
  • 4.2.1.3 Naive Bayes Classifier
    • Explanation of Naive Bayes Classifier and its applications
    • Methods for implementing Naive Bayes Classifier

4.2.2 Tree-Based Classifiers

  • 4.2.2.1 Decision Tree Classifier
    • Explanation of Decision Tree Classifier and its applications
    • Methods for implementing Decision Tree Classifier
  • 4.2.2.2 Random Forest Classifier
    • Explanation of Random Forest Classifier and its benefits
    • Methods for implementing Random Forest Classifier

4.2.3 Advanced Classification Models

  • 4.2.3.1 Support Vector Machines (SVM)
    • Explanation of Support Vector Machines and its scenarios
    • Methods for implementing Support Vector Machines
  • 4.2.3.2 Gradient Boosting (XGBoost, LightGBM, CatBoost)
    • Explanation of Gradient Boosting and its benefits
    • Overview of different Gradient Boosting libraries (XGBoost, LightGBM, CatBoost)
    • Methods for implementing Gradient Boosting algorithms

4.2.4 Neural and Ensemble Methods

  • 4.2.4.1 Neural Networks
    • Explanation of Neural Networks including MLP, CNN, RNN and their applications in classification
    • Methods for implementing Neural Networks for classification tasks
  • 4.2.4.2 Ensemble Methods
    • Explanation of Ensemble Methods such as Bagging, Boosting, and Stacking in classification
    • Methods for implementing Ensemble Methods for improving classification performance