{"id":17629823,"url":"https://github.com/arnaldog12/machine_learning","last_synced_at":"2025-05-07T15:06:38.726Z","repository":{"id":136994105,"uuid":"91585645","full_name":"arnaldog12/Machine_Learning","owner":"arnaldog12","description":"Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.","archived":false,"fork":false,"pushed_at":"2022-04-27T16:26:51.000Z","size":7161,"stargazers_count":223,"open_issues_count":2,"forks_count":62,"subscribers_count":22,"default_branch":"master","last_synced_at":"2025-05-07T15:06:23.513Z","etag":null,"topics":["adaboost","decision-trees","feature-selection","kmeans","knn","linear-discriminant-analysis","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","multilinear-regression","naive-bayes","neural-network","polynomial-regression","principal-component-analysis","python","redes-neurais-artificiais","regression"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arnaldog12.png","metadata":{"files":{"readme":"README.md","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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-05-17T14:29:10.000Z","updated_at":"2025-04-23T21:21:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"d0003b9f-20a5-4f92-9c23-05d00ee10385","html_url":"https://github.com/arnaldog12/Machine_Learning","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/arnaldog12%2FMachine_Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnaldog12%2FMachine_Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnaldog12%2FMachine_Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnaldog12%2FMachine_Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arnaldog12","download_url":"https://codeload.github.com/arnaldog12/Machine_Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252902614,"owners_count":21822261,"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":["adaboost","decision-trees","feature-selection","kmeans","knn","linear-discriminant-analysis","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","multilinear-regression","naive-bayes","neural-network","polynomial-regression","principal-component-analysis","python","redes-neurais-artificiais","regression"],"created_at":"2024-10-23T00:48:54.601Z","updated_at":"2025-05-07T15:06:38.700Z","avatar_url":"https://github.com/arnaldog12.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introdução\nEsse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português.\n\n# Algoritmos Implementados\n\n| Classificação | Regressão | Clusterização | Redução de \u003cbr\u003eDimensionalidade |\n|:----------------:|:--------------:|:-------------:|:-------------------------------:|\n| 🌿 Adaboost | 📈 Linear | 🔠 K-Means | 🌹 PCA |\n| 🌳 Decision Trees | 🔱 Multivariada | 🔠↖️ MeanShift | 🌻 LDA |\n|  🏠🏠 K-NN | 📊 Polinomial |  |  |\n| 🎲 Naive Bayes |  |  |  |\n| 💲 Regressão Logística |  |  |  |\n| 🧠 Redes Neurais | 🧠 Redes Neurais |  |  |\n\nE ainda temos um notebook só com métodos de **Seleção de Atributos**:\n\n| Métodos de Filtragem \u003cbr\u003e(Filter Methods) | Métodos de Empacotamento \u003cbr\u003e(Wrapper Methods) | Métodos Embarcados \u003cbr\u003e(Embedded Methods) |\n|:-------------------------------------:|:------------------------------------------:|:-------------------------------------:|\n| 📈 📉 Correlação de Pearson | 🏆 Stability Selection | 📈 Regressão Linear |\n| 📝 :left_right_arrow:📝 Mutual Information | 🔁 Eliminação Recursiva | 1️⃣ Regularização L1 (Lasso) |\n| 💯 Maximal Information Coefficient | ⭐️ Boruta | 2️⃣ Regularização L2 (Ridge) |\n|  |  | ⬇️ 💩 Mean Decrease Impurity |\n|  |  | ⬇️ 🎯 Mean Decrease Accuracy |\n\n\n# Instalação\n1. Baixe ou clone o repositório.\n2. Baixe e instale o [Miniconda](https://conda.io/miniconda.html). (__Windows__: marque a opção de adicionar o conda às variáveis de ambiente (_$PATH_))\n3. Abra o terminal e digite os seguintes comandos para instalar o ambiente:\n    ```sh\n    $ conda config --add channels bioconda\n    $ conda create -n ml python=3.5.3 numpy=1.12.1 pandas=0.20.1 matplotlib=2.0.2 scikit-learn=0.20.0 seaborn=0.7.1 jupyter=1.0.0 pydotplus==2.0.2\n    ```\n\n#### Uso do ambiente\n\n\u003e __Nota:  É obrigatório seguir as ordens da seção \"Instalação\" antes de utilizar o ambiente__.\n\nSiga os passos abaixo sempre que quiser executar os códigos desse repositório.\n1. Abra o terminal e digite:\n    - __Windows__:\n    ```sh\n    $ activate mpdl\n    ```\n    - __Linux/Mac__:\n    ```sh\n    $ source activate mpdl\n    ```\n2. Execute o Jupyter Notebook:\n    ```sh\n    $ jupyter notebook\n    ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnaldog12%2Fmachine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farnaldog12%2Fmachine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnaldog12%2Fmachine_learning/lists"}