Plano de aula disponível em: https://docs.google.com/document/d/1Xp9-U-ec9Mym1nBdfNXSe_ZhZM-f1V0tVBNjANJsXhk/edit?usp=sharing
[1] https://twitter.com/basedosdados
[2] 50 best public datasets - https://medium.com/towards-artificial-intelligence/the-50-best-public-datasets-for-machine-learning-d80e9f030279
[3] UCI ML Repository - http://archive.ics.uci.edu/ml/index.php
[4] ML Data - https://www.mldata.io/
[1] Python Data Science Handbook (Jake Vander Plas, 2016) O’Reilly
[2] Python Data Science Handbook Notebooks - https://bit.ly/2KDSs3Y
[3] Dealing With Unbalanced Data in Machine Learning - https://shiring.github.io/machine_learning/2017/04/02/unbalanced
[4] Cheat Sheet Scikit-Learn - https://bit.ly/34h3mEA
[5] Classification Metrics - https://scikit-learn.org/stable/modules/classes.html#classification-metrics
[6] Cross-Validation - https://scikit-learn.org/stable/modules/cross_validation.html
[7] Precision and Recall - https://en.wikipedia.org/wiki/Precision_and_recall
[8] Decision Tree - https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
[9] Árvores - https://github.com/jasjung/Machine_Learning/tree/master/*Machine_Learning/Trees
[9] Aprendizagem por Reforço - https://www.maxwell.vrac.puc-rio.br/19637/19637_4.PDF
[10] Oversampling - https://imbalanced-learn.readthedocs.io/en/stable/over_sampling.html
[11] When Algorithms Discriminate? - https://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html
[12] Clustering - https://scikit-learn.org/stable/modules/clustering.html
[13] Silhouette Score - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html
[14] K-Means e o Método do Cotovelo - https://medium.com/pizzadedados/kmeans-e-metodo-do-cotovelo-94ded9fdf3a9
[15] Ciência de Dados 1, por Nazareno Andrade - https://github.com/nazareno/ciencia-de-dados-1
[16] Evaluating ML Models Fairness and Bias - https://towardsdatascience.com/evaluating-machine-learning-models-fairness-and-bias-4ec82512f7c3
[17] Aequitas - https://dssg.github.io/aequitas/
[1] Seeing Theory - https://seeing-theory.brown.edu/
[2] Fast.ai - https://www.fast.ai/2018/09/26/ml-launch/
[3] Feature Engineering and Selection (Max Kuhn and Kjell Johnson) - http://www.feat.engineering/index.html
[4] Machine Learning Crash Course - https://developers.google.com/machine-learning/crash-course/
[5] Dicas de Machine Learning CS 229 Stanford- https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/pt
[6] A tour of ML Algorithms - https://www.datasciencecentral.com/profiles/blogs/a-tour-of-machine-learning-algorithms-1
[7] How to handle imbalanced classes in ML - https://elitedatascience.com/imbalanced-classes
[8] Coeficiente de Correlação de Matthew - https://lettier.github.io/posts/2016-08-05-matthews-correlation-coefficient.html
[9] Regressão Logística Passo a Passo - https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8
[10] Understanding DTs once and for all - https://towardsdatascience.com/understanding-decision-trees-once-and-for-all-2d891b1be579
[11] Random Forest in Python - https://towardsdatascience.com/random-forest-in-python-24d0893d51c0
[12] Breast Cancer Data - https://www.mldata.io/dataset-details/breast_cancer/#customize_download
[13] Teachable Machine - https://teachablemachine.withgoogle.com/
[14] MLCC Fairness - https://developers.google.com/machine-learning/crash-course/fairness/types-of-bias
[15] Understanding ROC and AUC curves - https://towardsdatascience.com/understanding-the-roc-and-auc-curves-a05b68550b69