The mission of this repository is to provide a comprehensive and organized collection of information on commonly used Python libraries in the field of Machine Learning. The repository is designed to be user-friendly and easily accessible, with each section covering a specific topic related to Machine Learning, such as data preprocessing, supervised learning, and hyperparameter tuning. Our goal is to enable individuals, whether they are beginners or experts in the field of Machine Learning, to find the information they need quickly and efficiently. We strive to ensure that the information in this repository is up-to-date and accurate, so that users can confidently rely on it in their work and research.
Machine Learning has become an increasingly popular field in recent years, with applications ranging from speech recognition and image classification to fraud detection and natural language processing. As the field has grown, so too have the number of Python libraries available to developers and researchers. This repository is a collection of information on some of the most commonly used Python libraries in the field of Machine Learning, organized into sections based on specific topics. Whether you are new to Machine Learning or an experienced practitioner, this repository is a valuable resource that can help you find the right library for your needs. With information on topics such as data preprocessing, supervised learning, and hyperparameter tuning, this repository provides a comprehensive overview of the Python libraries that are essential to anyone working in the field of Machine Learning.
-
Data Preprocessing: This section covers the basics of data preprocessing in Machine Learning.
-
Supervised Learning: This section covers the basics of supervised learning in Machine Learning.
-
Regression: This section covers different regression techniques such as linear regression, simple linear regression, multiple linear regression, polynomial regression, support vector regression, and decision trees regression.
-
Classification: This section covers the basics of classification in Machine Learning, binary and multi-class classifier, and the different learners in classification problems such as lazy and eager learners.
-
Evaluating a Classification Model: This section covers different evaluation metrics such as log loss or cross-entropy loss, confusion matrix, and AUC-ROC curve.
-
Linear Model: This section covers different linear models such as logistic regression, binomial, multinomial, ordinal.
-
Non-linear Model: This section covers different non-linear models such as K-Nearest Neighbors (KNN), decision trees, random forest, and Naïve Bayes.
-
Dimensionality Reduction: This section covers different dimensionality reduction techniques such as filters, wrappers, embedded methods, Lasso Regression, Ridge Regression, feature extraction, and principal component analysis (PCA).
-
Unsupervised Learning: This section covers different unsupervised learning techniques such as clustering, partitioning clustering, density-based clustering, distribution model-based clustering, hierarchical clustering/agglomerative hierarchical clustering, fuzzy clustering, and association.
-
Hyperparameter Tuning: This section covers different approaches for hyperparameter tuning such as train_test_split, K Fold Cross validation, GridSearchCV, RandomizedSearchCV, and using different models with different parameters.
-
Bagging/Ensemble Learning: This section covers different ensemble learning techniques such as bagging.
HTML format
Markdown format
PDF format
- [2022.08.15]: Release the project.
- [2023.09.03]: Update the main content.
- [2023.02.01]: Update the remaining content on Supervised Learning.
- [2023.04.17]: Finalize the project details.
If your have any comments or questions, feel free to contact kwokhinchi@gmail.com
Python Libraries for Machine Learning is an open source project, contributed by our team. We thank all contributors who implemented their methods or added new features, as well as users who provided valuable feedback. We hope for further implementation and improvement of these systems.
Permission is hereby granted, free of charge, to any person obtaining a copy of this Repository and associated documentation files, to deal in the Repository without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Repository is furnished to do so.
The authors or copyright holders are not be liable for any claim, damages or other liabillty, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the Repository or the use or other dealings in the Repository.
This repository is for personal/research/non-commercial use only.
Copyright © Haley Kwok. All rights reserved.
Credit: Materials learned from @Codebasics