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mkdocs.yml
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# Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>
# Project information
site_name: Home
site_url: https://github.com/fralfaro/MAT281_2022
site_author: Francisco Alfaro
site_description:
# Repository
repo_name: fralfaro/MAT281_2022
repo_url: https://github.com/fralfaro/MAT281_2022
edit_uri: ''
# Markdown extensions
markdown_extensions:
- attr_list
- admonition
- md_in_html
- pymdownx.arithmatex:
generic: true
- pymdownx.highlight:
anchor_linenums: true
- pymdownx.inlinehilite
- pymdownx.snippets
- pymdownx.superfences
- pymdownx.tasklist
- pymdownx.tabbed
# Extra javascript
extra_javascript:
- javascripts/mathjax.js
- https://polyfill.io/v3/polyfill.min.js?features=es6
- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
# Theme
theme:
name: material
language: es
logo: images/logo.bmp
favicon: images/utfsm.png
features:
- navigation.instant
- navigation.top
- content.code.annotate
- search.suggest
- search.highlight
palette:
# Light mode
- media: '(prefers-color-scheme: light)'
scheme: default
primary: blue grey
accent: light blue
toggle:
icon: material/toggle-switch-off-outline
name: Switch to dark mode
# Dark mode
- media: '(prefers-color-scheme: dark)'
scheme: slate
primary: blue grey
accent: light blue
toggle:
icon: material/toggle-switch
name: Switch to light mode
# Customization
extra:
social:
- icon: fontawesome/brands/github
link: https://github.com/fralfaro
- icon: fontawesome/brands/gitlab
link: https://gitlab.com/fralfaro
- icon: fontawesome/brands/linkedin
link: https://www.linkedin.com/in/faam/
- icon: fontawesome/brands/kaggle
link: https://www.kaggle.com/faamds
- icon: fontawesome/brands/medium
link: https://medium.com/@fralfaro
- icon: fontawesome/solid/globe
link: https://fralfaro.github.io/portfolio/
# Plugins
plugins:
- mkdocs-jupyter:
kernel_name: python3
- search
# TOC
nav:
- index.md
- Basic Tools:
- Tools:
- lectures/basic_tools/basic_tools/lecture_000_intro.ipynb
- lectures/basic_tools/basic_tools/lecture_000_configuraciones.ipynb
- Linux: lectures/basic_tools/basic_tools/lecture_011_os.ipynb
- Git: lectures/basic_tools/basic_tools/lecture_015_git.ipynb
- IDE: lectures/basic_tools/basic_tools/lecture_013_ide.ipynb
- Python: lectures/basic_tools/basic_tools/lecture_012_python.ipynb
- Jupyter Notebook: lectures/basic_tools/basic_tools/lecture_014_jupyter.ipynb
- Google Colab: lectures/basic_tools/basic_tools/lecture_016_colab.ipynb
- Scientific Computing:
- Numpy: lectures/data_manipulation/scientific_computing/numpy.ipynb
- Scipy: lectures/data_manipulation/scientific_computing/scipy.ipynb
- Sympy: lectures/data_manipulation/scientific_computing/sympy.ipynb
- Data manipulation:
- Introducción: lectures/data_manipulation/data_manipulation/base_datos.ipynb
- Pandas:
- lectures/data_manipulation/data_manipulation/pandas.ipynb
- lectures/data_manipulation/data_manipulation/groupby.ipynb
- lectures/data_manipulation/data_manipulation/merge_concat.ipynb
- lectures/data_manipulation/data_manipulation/pivot.ipynb
- Visualization:
- Introducción: lectures/data_manipulation/visualization/01_intro.ipynb
- Matplotlib: lectures/data_manipulation/visualization/matplotlib.ipynb
- Seaborn: lectures/data_manipulation/visualization/seaborn.ipynb
- EDA:
- lectures/data_manipulation/eda/intro.md
- lectures/data_manipulation/eda/eda.ipynb
- Machine Learning:
- Introducción: lectures/ml/introduccion_ml/introduccion.ipynb
- Aprendizaje Supervisado I:
- lectures/ml/analisis_supervisado_regresion/intro.md
- lectures/ml/analisis_supervisado_regresion/02_regresion_lineal.ipynb
- lectures/ml/analisis_supervisado_regresion/02_modelos_regresion.ipynb
- Aprendizaje Supervisado II:
- lectures/ml/analisis_supervisado_clasificacion/intro.md
- lectures/ml/analisis_supervisado_clasificacion/02_clasificacion.ipynb
- Aprendizaje No supervisado:
- lectures/ml/analisis_no_supervisado/intro.md
- lectures/ml/analisis_no_supervisado/03_clustering.ipynb
- lectures/ml/analisis_no_supervisado/03_reduccion_dimensionalidad.ipynb
- Overffiting:
- lectures/ml/overfitting/04_overfitting_underfitting.ipynb
- lectures/ml/overfitting/04_reducir_overfitting.ipynb
- Series Temporales: lectures/ml/time_series/05_series_temporales.ipynb