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domain	cienciadedatos.net
summary	This website provides a comprehensive introduction to machine learning techniques using Python and R, covering a wide range of algorithms and evaluation methods. Key topics include: * Regression: Linear Regression, Logistic Regression, Regularization (Ridge, Lasso, Elastic Net), Quantile Regression, Non-Linear Regression (Polynomial Regression, Splines, GAMs). * Tree-Based Methods: Decision Trees, Random Forests, Gradient Boosting (including Probabilistic Gradient Boosting), C5.0. * Support Vector Machines: SVM. * Neural Networks: Basic Neural Networks (with Python). * Model Interpretation: ICE and PDP plots. * Model Calibration: Techniques for calibrating machine learning models. * Model Validation: Cross-Validation, Leave-One-Out, Bootstrap. * Advanced Techniques: Algorithm Genetic for Feature Selection, H2O, IML, DALEX, Bayesian Optimization, Association Rules (Apriori), Recommendation Systems. The site offers implementations and examples using libraries like Scikit-learn, R, Caret, tidymodels, mlr3, and H2O.
title	Data science, theory and practical examples in R and Python
description	Tutorials and examples on statistics, algorithms, machine learning, data science, artificial intelligence and programming in R and Python
keywords	python, learning, forecasting, machine, test, para, leer, forest, series, gradient, anova, regression, vector, clustering, data, sponsors, material
upstreams
downstreams	paypal.com, github.com, linkedin.com, creativecommons.org, youtube.com
nslookup	A 35.157.26.135, A 63.176.8.218
created	2024-10-17
updated	2026-01-01
summarized	2026-01-30

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