Practicas del curso de Modelos predictivos con Machine Learning de la UVA.
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Practicas del curso de Modelos predictivos con Machine Learning de la UVA.
Email: sergio.alegre.arribas EN gmail.com
LinkedIn: https://www.linkedin.com/in/sergioalegre
Website: http://me.sergioalegre.es
- Python
- Machine Learning
- Regresion Lineal / Linear Regression
- Regresion Polinomial / Polinomial Regression.
- SVR / Support Vector Regression.
- Regresion Logistica con Resampling.
- Matriz de confusion / Confusion Matrix
- Variables Dummy
- Selección de caracteristicas
- Clustering con K-means
- Series de tiempo / Time Series
- Ejemplos sencillos de diversas técnicas de aprendizaje de diferentes datasets populares. Ejemplos de Regresión lineal, SVR
- Simple example of diffentent ML techniques using popular datasets. Examples based Linear Regression, Random Forest, Support Vector Machine, K-Means Clustering and Tensorflow.
- Anaconda para ejecurtar los Juniper notebooks / R Studio o cuenta en Colab o servicio similar.
- Anaconda to run Juniper notebooks / R Studio o have a Colab account or similar service.
- Solamente instalar Anaconda e instalar las librerias si alguna faltara.
- Just install Anaconda and install, if needed, any missing dependency (library).
- Simplemente importa el Juniper notebook o el archivo .R
- Just import notebook or .R file.
- En este repo iré almacenando más ejemplos comentados.
- I'll add more examples.
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.