{"id":13471911,"url":"https://github.com/rasbt/pattern_classification","last_synced_at":"2025-05-14T14:08:14.336Z","repository":{"id":15523822,"uuid":"18258282","full_name":"rasbt/pattern_classification","owner":"rasbt","description":"A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks","archived":false,"fork":false,"pushed_at":"2023-11-26T15:54:59.000Z","size":112927,"stargazers_count":4171,"open_issues_count":0,"forks_count":1285,"subscribers_count":386,"default_branch":"master","last_synced_at":"2025-04-12T03:45:15.315Z","etag":null,"topics":["machine-learning","machine-learning-algorithms","pattern-classification"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rasbt.png","metadata":{"files":{"readme":"README.ipynb","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2014-03-30T05:34:34.000Z","updated_at":"2025-04-11T22:55:08.000Z","dependencies_parsed_at":"2022-07-26T02:32:04.877Z","dependency_job_id":"e8947736-0123-4613-9c0a-962d012a7b38","html_url":"https://github.com/rasbt/pattern_classification","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpattern_classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpattern_classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpattern_classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpattern_classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rasbt","download_url":"https://codeload.github.com/rasbt/pattern_classification/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254159691,"owners_count":22024564,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","machine-learning-algorithms","pattern-classification"],"created_at":"2024-07-31T16:00:50.285Z","updated_at":"2025-05-14T14:08:14.317Z","avatar_url":"https://github.com/rasbt.png","language":"Jupyter Notebook","readme":"{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![logo](./Images/logo.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\u003chr\u003e\\n\",\n    \"**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.**\\n\",\n    \"\u003chr\u003e\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Table of Contents\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- [Introduction to Machine Learning and Pattern Classification](#Introduction-to-Machine-Learning-and-Pattern-Classification)\\n\",\n    \"- [Pre-Processing](#Pre-Processing)\\n\",\n    \"- [Model Evaluation](#Model-Evaluation)\\n\",\n    \"- [Parameter Estimation](#Parameter-Estimation)\\n\",\n    \"- [Machine Learning Algorithms](#Machine-Learning-Algorithms)\\n\",\n    \"\\t- [Bayes Classification](#Bayes-Classification)\\n\",\n    \"\\t- [Logistic Regression](#Logistic-Regression)\\n\",\n    \"\\t- [Neural Networks](#Neural-Networks)\\n\",\n    \"\\t- [Ensemble Methods](#Ensemble-Methods)\\n\",\n    \"- [Statistical Pattern Classification Examples](#Statistical-Pattern-Classification-Examples)\\n\",\n    \"- [Clustering](#Clustering)\\n\",\n    \"- [Collecting Data](#Collecting-Data)\\n\",\n    \"- [Resources](#Resources)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Introduction to Machine Learning and Pattern Classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Predictive modeling, supervised machine learning, and pattern classification - the big picture [[Markdown]](machine_learning/supervised_intro/introduction_to_supervised_machine_learning.md)\\n\",\n    \"* Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb]](machine_learning/scikit-learn/python_data_entry_point.ipynb)\\n\",\n    \"* An Introduction to simple linear supervised classification using `scikit-learn` [[IPython nb]](machine_learning/scikit-learn/scikit_linear_classification.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Pre-Processing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* **Feature Extraction**\\n\",\n    \"    * Tips and Tricks for Encoding Categorical Features in Classification Tasks [[IPython nb]](preprocessing/feature_encoding.ipynb)\\n\",\n    \"* **Scaling and Normalization**\\n\",\n    \"    * About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb]](preprocessing/about_standardization_normalization.ipynb)\\n\",\n    \"* **Feature Selection**\\n\",\n    \"    * Sequential Feature Selection Algorithms [[IPython nb]](dimensionality_reduction/feature_selection/sequential_selection_algorithms.ipynb)\\n\",\n    \"* **Dimensionality Reduction**\\n\",\n    \"    * Principal Component Analysis (PCA) [[IPython nb]](dimensionality_reduction/projection/principal_component_analysis.ipynb)\\n\",\n    \"    * PCA based on the covariance vs. correlation matrix [[IPython nb]](dimensionality_reduction/projection/pca_cov_cor.ipynb)\\n\",\n    \"    * Linear Discriminant Analysis (LDA) [[IPython nb]](dimensionality_reduction/projection/linear_discriminant_analysis.ipynb)\\n\",\n    \"    * The effect of scaling and mean centering of variables prior to a PCA [[PDF]](./dimensionality_reduction/projection/scale_center_pca/scale_center_pca.pdf)    \\n\",\n    \"    * Kernel tricks and nonlinear dimensionality reduction via PCA [[IPython nb]](dimensionality_reduction/projection/kernel_pca.ipynb)\\n\",\n    \"*  **Representing Text**\\n\",\n    \"\\t* Tf-idf Walkthrough for scikit-learn [[IPython nb](./machine_learning/scikit-learn/tfidf_scikit-learn.ipynb)]    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* An Overview of General Performance Metrics of Binary Classifier Systems [[PDF](./evaluation/performance_metrics/performance_metrics.pdf)]\\n\",\n    \"* **Cross-Validation**\\n\",\n    \"    * Streamline your cross-validation workflow - scikit-learn's Pipeline in action [[IPython nb]](machine_learning/scikit-learn/scikit-pipeline.ipynb)\\n\",\n    \"* Model evaluation, model selection, and algorithm selection in machine learning - Part I [[Markdown]](./evaluation/model-evaluation/model-evaluation-selection-part1.md)\\n\",\n    \"* Model evaluation, model selection, and algorithm selection in machine learning - Part II [[Markdown]](./evaluation/model-evaluation/model-evaluation-selection-part2.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Parameter Estimation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* **Parametric Techniques**\\n\",\n    \"    * Introduction to the Maximum Likelihood Estimate (MLE) [[IPython nb]](parameter_estimation_techniques/maximum_likelihood_estimate.ipynb)\\n\",\n    \"    * How to calculate Maximum Likelihood Estimates (MLE) for different distributions [[IPython nb]](parameter_estimation_techniques/max_likelihood_est_distributions.ipynb)\\n\",\n    \"\\n\",\n    \"* **Non-Parametric Techniques**\\n\",\n    \"    * Kernel density estimation via the Parzen-window technique [[IPython nb]](parameter_estimation_techniques/parzen_window_technique.ipynb)\\n\",\n    \"    * The K-Nearest Neighbor (KNN) technique\\n\",\n    \"\\n\",\n    \"* **Regression Analysis**\\n\",\n    \"    * Linear Regression\\n\",\n    \"        * Least-Squares fit [[IPython nb]](data_fitting/regression/linregr_least_squares_fit.ipynb)\\n\",\n    \"    * Non-Linear Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Machine Learning Algorithms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Bayes Classification\\n\",\n    \"\\n\",\n    \"- Naive Bayes and Text Classification I - Introduction and Theory [[View PDF](http://sebastianraschka.com/PDFs/articles/naive_bayes_1.pdf)] [[Download PDF](./machine_learning/naive_bayes_1/tex/naive_bayes_1.pdf)] \\n\",\n    \"\\n\",\n    \"#### Logistic Regression\\n\",\n    \"\\n\",\n    \"- Out-of-core Learning and Model Persistence using scikit-learn\\n\",\n    \"[[IPython nb](./machine_learning/scikit-learn/outofcore_modelpersistence.ipynb)]\\n\",\n    \"\\n\",\n    \"#### Neural Networks\\n\",\n    \"\\n\",\n    \"- Artificial Neurons and Single-Layer Neural Networks - How Machine Learning Algorithms Work Part 1 [[IPython nb](./machine_learning/singlelayer_neural_networks/singlelayer_neural_networks.ipynb)]\\n\",\n    \"\\n\",\n    \"- Activation Function Cheatsheet [[IPython nb](./machine_learning/neural_networks/ipynb/activation_functions.ipynb)]\\n\",\n    \"\\n\",\n    \"#### Ensemble Methods\\n\",\n    \"\\n\",\n    \"- Implementing a Weighted Majority Rule Ensemble Classifier in scikit-learn  [[IPython nb](./machine_learning/scikit-learn/ensemble_classifier.ipynb)]\\n\",\n    \"\\n\",\n    \"#### Decision Trees\\n\",\n    \"\\n\",\n    \"- Cheatsheet for Decision Tree Classification [[IPython nb]('./machine_learning/decision_trees/decision-tree-cheatsheet.ipynb')]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- **Protoype-based clustering**\\n\",\n    \"- **Hierarchical clustering**\\n\",\n    \"\\t- Complete-Linkage Clustering and Heatmaps in Python [[IPython nb](./clustering/hierarchical/clust_complete_linkage.ipynb)]\\n\",\n    \"- **Density-based clustering**\\n\",\n    \"- **Graph-based clustering**\\n\",\n    \"- **Probabilistic-based clustering**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Collecting Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Collecting Fantasy Soccer Data with Python and Beautiful Soup [[IPython nb](./data_collecting/parse_dreamteamfc_data.ipynb)]\\n\",\n    \"\\n\",\n    \"- Download Your Twitter Timeline and Turn into a Word Cloud Using Python [[IPython nb](./data_collecting/twitter_wordcloud.ipynb)]\\n\",\n    \"\\n\",\n    \"- Reading MNIST into NumPy arrays [[IPython nb](./data_collecting/reading_mnist.ipynb)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Statistical Pattern Classification Examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* **Supervised Learning**\\n\",\n    \"    * Parametric Techniques\\n\",\n    \"        * Univariate Normal Density\\n\",\n    \"            * Ex1: 2-classes, equal variances, equal priors [[IPython nb]](stat_pattern_class/supervised/parametric/1_stat_superv_parametric.ipynb)\\n\",\n    \"            * Ex2: 2-classes, different variances, equal priors [[IPython nb]](stat_pattern_class/supervised/parametric/2_stat_superv_parametric.ipynb)\\n\",\n    \"            * Ex3: 2-classes, equal variances, different priors [[IPython nb]](stat_pattern_class/supervised/parametric/3_stat_superv_parametric.ipynb)\\n\",\n    \"            * Ex4: 2-classes, different variances, different priors, loss function [[IPython nb]](stat_pattern_class/supervised/parametric/4_stat_superv_parametric.ipynb)\\n\",\n    \"            * Ex5: 2-classes, different variances, equal priors, loss function, cauchy distr.[[IPython nb]](stat_pattern_class/supervised/parametric/5_stat_superv_parametric.ipynb)\\n\",\n    \"\\n\",\n    \"        * Multivariate Normal Density\\n\",\n    \"            * Ex5: 2-classes, different variances, equal priors, loss function [[IPython nb]](stat_pattern_class/supervised/parametric/5_stat_superv_parametric.ipynb)\\n\",\n    \"            * Ex7: 2-classes, equal variances, equal priors [[IPython nb]](stat_pattern_class/supervised/parametric/7_stat_superv_parametric.ipynb)\\n\",\n    \"\\n\",\n    \"    * Non-Parametric Techniques\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Resources\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Matplotlib examples - Visualization techniques for exploratory data analysis [[IPython nb]](resources/matplotlib_viz_gallery.ipynb)\\n\",\n    \"\\n\",\n    \"* Copy-and-paste ready LaTex equations [[Markdown]](resources/latex_equations.md)\\n\",\n    \"\\n\",\n    \"* Open-source datasets [[Markdown]](resources/dataset_collections.md)\\n\",\n    \"\\n\",\n    \"* Free Machine Learning eBooks [[Markdown]](resources/machine_learning_ebooks.md)\\n\",\n    \"\\n\",\n    \"* Terms in data science defined in less than 50 words [[Markdown]](resources/data_glossary.md)\\n\",\n    \"\\n\",\n    \"* Useful libraries for data science in Python [[Markdown]](resources/python_data_libraries.md)\\n\",\n    \"\\n\",\n    \"* General Tips and Advices [[Markdown]](resources/general_tips_and_advices.md)\\n\",\n    \"\\n\",\n    \"* A matrix cheatsheat for Python, R, Julia, and MATLAB [[HTML]](http://sebastianraschka.com/github/pattern_classification/matrix_cheatsheet_table.html)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n","funding_links":[],"categories":["Uncategorized","Jupyter Notebook","Technical","Python","machine-learning","📚 Project Purpose"],"sub_categories":["Uncategorized","ramanihiteshc@gmail.com","General-Purpose Machine Learning","Machine Learning (Intermediate-Level"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fpattern_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frasbt%2Fpattern_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fpattern_classification/lists"}