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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