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The Machine Learning Repository is a comprehensive collection of resources, code snippets, and projects focused on various aspects of machine learning. This repository specifically emphasizes ensemble methods, regression, classification, hyperparameter tuning, clustering and feature engineering.

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Machine Learning Repository

This repository contains code and examples for various machine learning tasks.

Table of Contents

Ensemble Learning

This folder contains examples and implementations of ensemble learning methods, including:

  • Bagging
  • Boosting
  • Voting
  • Random forests

Classification

This folder contains examples and implementations of classification algorithms, including:

  • Logistic regression
  • Decision trees
  • Support vector machines
  • Naive Bayes
  • k-Nearest Neighbors
  • Random Forests

Regression

This folder contains examples and implementations of regression algorithms, including:

  • Linear regression
  • Ridge regression
  • Lasso regression
  • ElasticNet regression
  • Support Vector Machine
  • Stochastic Gradient Descent
  • Random Forest Regression

Feature Engineering

This folder contains examples and implementations of feature engineering techniques, including:

  • Feature scaling and normalization
  • Principal component analysis (PCA)
  • t-SNE
  • Feature selection

Hyperparameter Tuning

This folder contains examples and implementations of hyperparameter tuning methods, including:

  • Grid search
  • Random search
  • Bayesian optimization

Clustering

This folder contains examples and implementations of clustering algorithms, including:

  • k-Means
  • Hierarchical clustering
  • DBSCAN
  • Gaussian mixture models

About

The Machine Learning Repository is a comprehensive collection of resources, code snippets, and projects focused on various aspects of machine learning. This repository specifically emphasizes ensemble methods, regression, classification, hyperparameter tuning, clustering and feature engineering.

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