In this repository it presents Scikit-learn libraries for the development of Machine Learning in Python so that they can deepen or incorporate into their projects, within which I describe features of the models: PCA and IPCA, Kernels and KPCA, regularization through Lasso and Ridge , ElasticNet, outlier resolution, data preparation for robust regressions, assembly methods, and data preparation for these methods, Bagging, Boosting implementation, clustering strategies, Batch K-Means and Mean-Shhift, Cross Validation, and use K-Folds, paramedical optimization, implementation of Randomized, exit to production with API in Flask If you have any questions and / or comments, you can contact me at my email:
[email protected] Thank you
