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Recopilatorio Data Science

Recopilatorio de materiales relacionados con Data Science, Machine Learning, Deep Learning, Inteligencia artificial y todo lo relacionado con esas materias en la red

Contenido

  1. Recursos
  2. Programas/Webs suite
  3. Datasets
  4. Modelos preentrenados
  5. APIs
  6. Libros y documentos
  7. Blogs
  8. Canales de Youtube
  9. Plataformas de aprendizaje
  10. Otros programas/recursos
  11. Paquetes R
  12. Paquetes Python
  13. Casos de uso
  14. Automated machine learning
  15. Embedded machine learning
  16. Spatial Data Science / Machine Learning
  17. Herramientas de Anotación

Recursos

Google AI education https://ai.google/education/

Microsoft AI school https://aischool.microsoft.com/en-us/home

Intel AI academy https://software.intel.com/es-es/ai-academy/students/courses

NVIDIA Deep Learning Institute (https://www.nvidia.com/es-es/deep-learning-ai/education/)

IBM cognitive class https://cognitiveclass.ai/

Facebook Machine Learning https://research.fb.com/category/machine-learning/

Amazon Machine Learning https://aws.amazon.com/es/aml/

Kaggle https://www.kaggle.com/learn/overview

Fast.ai https://www.fast.ai/

Mlcourse.ai https://mlcourse.ai/

Datacamp https://www.datacamp.com/

Dataquest https://www.dataquest.io/

KDnuggets https://www.kdnuggets.com/

Data Science Central https://www.datasciencecentral.com/

Analytics Vidhya https://www.analyticsvidhya.com/blog/

Towards data science https://towardsdatascience.com/

Business Science University https://university.business-science.io/

Quick-R https://www.statmethods.net/index.html

RDM http://www.rdatamining.com/

Capacítate para el empleo (Tecnología) https://capacitateparaelempleo.org

School of AI https://www.theschool.ai/

DataFlair https://data-flair.training/blogs/

Elements of IA https://course.elementsofai.com/

Made With ML (https://madewithml.com/)

New Deep Learning Techniques (http://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule)

AI Institute Geometry of Deep Learning (https://www.microsoft.com/en-us/research/event/ai-institute-2019/#!videos)

Machine Learning Práctico (https://elsonidoq.github.io/machine-learning-practico/)


Universidades

Statistics, Probability & Data Science

Machine Learning

Computer Vision

Deep Learning

Natural Language Processing

Reinforcement Learning

Artificial Intelligence

Algorithmics

Programas/Webs suite

Power BI (https://powerbi.microsoft.com/es-es/)

Knime (https://www.knime.com/)

Weka (https://www.cs.waikato.ac.nz/ml/weka/)

Orange (https://orange.biolab.si/)

RapidMiner (https://rapidminer.com/)

BIRT (https://www.eclipse.org/birt/)

Jaspersoft (https://www.jaspersoft.com/es/soluciones)

Microsoft Azure (https://docs.microsoft.com/es-es/learn/azure/)

IBM Watson Studio (https://www.ibm.com/cloud/watson-studio)

Tableau (https://www.tableau.com/es-es)

Metabase (https://metabase.com/)

Knowage (https://www.knowage-suite.com/site/home/)

Pentaho (Hitachi Vintara) (https://sourceforge.net/projects/pentaho/)

DeepCognition (https://deepcognition.ai/)

Google Data Studio (https://datastudio.google.com/overview)

Qlik (https://www.qlik.com/us)

Zoho Analytics (https://www.zoho.com/es-xl/analytics/)

Google Cloud Datalab (https://cloud.google.com/datalab/)

DataRobot (https://www.datarobot.com/)

Dataiku Data Science Studio (DSS) (https://www.dataiku.com/dss/)

BigML (https://bigml.com/)

Google Colaboratory (https://colab.research.google.com/notebooks/welcome.ipynb)

MLflow (https://mlflow.org/)

MLkit app mobile (https://developers.google.com/ml-kit/)


Datasets

Imágenes

Google repositorio imágenes etiquetadas (https://storage.googleapis.com/openimages/web/index.html)

Visión artificial (https://www.visualdata.io/)

Biometría (http://openbiometrics.org/)

COCO (https://cocodataset.org/#home)

ImageNet (http://www.image-net.org/)

Gestos - HaGRID - HAnd Gesture Recognition Image Dataset (https://github.com/hukenovs/hagrid)


Audio

Google repositorio de audio (https://research.google.com/audioset/)

Common Voice (https://commonvoice.mozilla.org/)

VoxForge (http://voxforge.org/es)


Texto

The Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/)

Amazon question/answer data (http://jmcauley.ucsd.edu/data/amazon/qa/)

Recommender Systems Datasets (https://cseweb.ucsd.edu/~jmcauley/datasets.html)

El corpus del español (https://www.corpusdelespanol.org/xs.asp)

MAS Corpus (Corpus for Marketing Analysis in Spanish) (http://mascorpus.linkeddata.es/)


Finanzas

Quandl (https://www.quandl.com/)


Conducción autónoma y coches

Waymo (https://waymo.com/open/)

KITTI 360 (http://www.cvlibs.net/datasets/kitti-360/)

CompCars Dataset (https://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html)

Stanford Cars Dataset (https://ai.stanford.edu/~jkrause/cars/car_dataset.html)

BDD100K (https://www.bdd100k.com/)

SHIFT (https://www.vis.xyz/shift/)


Datos científicos/investigación

Repositorios de datos recomendados por Scientific Data (https://www.nature.com/sdata/policies/repositories)

Mendeley Data (https://data.mendeley.com/)

Figshare (https://figshare.com/)

Dryad Digital Repository (https://datadryad.org/stash)

Harvard Dataverse (https://dataverse.harvard.edu/)

Open Scientific Framework (https://osf.io/)

Zenodo (https://zenodo.org/)

Open Acces Directory (http://oad.simmons.edu/oadwiki/Data_repositories)

IEEE DataPort (https://ieee-dataport.org/datasets)


UAVs / Drones

Open Aerial Map (https://openaerialmap.org/)

Semantic Drone Dataset (https://www.tugraz.at/index.php?id=22387)

SenseFly Datasets (https://www.sensefly.com/education/datasets/)

VisDrone Dataset (https://github.com/VisDrone/VisDrone-Dataset)

UAVid (https://uavid.nl/)

Rice Seedling Dataset (https://github.com/aipal-nchu/RiceSeedlingDataset)

UAV Sugarbeets 2015-16 Datasets (https://www.ipb.uni-bonn.de/data/uav-sugarbeets-2015-16/)

ReforesTree (https://github.com/gyrrei/ReforesTree)


Agricultura de precisión

Plant Pathology 2021 - FGVC8 (https://www.kaggle.com/c/plant-pathology-2021-fgvc8/overview)

PlantVillage Dataset (https://www.kaggle.com/abdallahalidev/plantvillage-dataset)

PlantDoc: A Dataset for Visual Plant Disease Detection (https://github.com/pratikkayal/PlantDoc-Dataset)

Plants_Dataset[99 classes] (https://www.kaggle.com/muhammadjawad1998/plants-dataset99-classes)

V2 Plant Seedlings Dataset (https://www.kaggle.com/vbookshelf/v2-plant-seedlings-dataset)

Eden Library (https://edenlibrary.ai/datasets)

Pl@ntNet - Base de datos e identificación de plantas (https://identify.plantnet.org/es)

A Crop/Weed Field Image Dataset (https://github.com/cwfid/dataset)

DeepWeeds (https://github.com/AlexOlsen/DeepWeeds)

Identification of Plant Leaf Diseases (https://data.mendeley.com/datasets/tywbtsjrjv/1)

Perrenial Plants Detection (https://www.kaggle.com/benediktgeisler/perrenial-plants-detection)

Global Wheat Challenge 2021 (https://www.kaggle.com/bendvd/global-wheat-challenge-2021)

Flower Recognition (https://www.kaggle.com/aymenktari/flowerrecognition)

Potato Disease Leaf Dataset(PLD) (https://www.kaggle.com/rizwan123456789/potato-disease-leaf-datasetpld)

Potato and weeds (https://www.kaggle.com/jchrysanthemum/potato-and-weeds)

Potato Plants Dataset (https://www.kaggle.com/ali7432/potato-plants-dataset)

Potato Leaf Annotation (https://www.kaggle.com/rizwan123456789/potato-leaf-annotation)

Cotton-Diseased or Fresh (https://www.kaggle.com/ananysharma/diseasecotton)

Cucumber plant diseases dataset (https://www.kaggle.com/kareem3egm/cucumber-plant-diseases-dataset)

Rice Leaf Diseases Dataset (https://www.kaggle.com/vbookshelf/rice-leaf-diseases)

Rice Plant Dataset (https://www.kaggle.com/rajkumar898/rice-plant-dataset)

Tomato Cultivars (https://www.kaggle.com/olgabelitskaya/tomato-cultivars)

AppleScabFDs (https://www.kaggle.com/projectlzp201910094/applescabfds)

AppleScabLDs (https://www.kaggle.com/projectlzp201910094/applescablds)

Hops Classification (https://www.kaggle.com/scruggzilla/hops-classification)

Plant semantic segmentation (https://www.kaggle.com/humansintheloop/plant-semantic-segmentation)

Synthetic RGB-D data for plant segmentation (https://www.kaggle.com/harlequeen/synthetic-rgbd-images-of-plants)

Synthetic RGB Data for Grapevine Detection (https://www.kaggle.com/carmenca/synthetic-rgb-data-for-grapevine-detection)

Leaf disease segmentation dataset (https://www.kaggle.com/fakhrealam9537/leaf-disease-segmentation-dataset)

Weed-AI Datasets (https://weed-ai.sydney.edu.au/datasets)

Plant Phenotyping Datasets (https://www.plant-phenotyping.org/datasets-home)

MinneApple: A Benchmark Dataset for Apple Detection and Segmentation (https://github.com/nicolaihaeni/MinneApple)

Embrapa Wine Grape Instance Segmentation Dataset (https://github.com/thsant/wgisd)

A Large-Scale Benchmark Dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102)

Sugar Beets 2016 (https://www.ipb.uni-bonn.de/data/sugarbeets2016/)

TobSet: Tobacco Crop and Weeds Image Dataset (https://github.com/mshahabalam/TobSet)

WE3DS: An RGB-D image dataset for semantic segmentation in agriculture (https://zenodo.org/record/7457983)


Ganadería de precisión

Animal Pose (vaca/oveja/caballo) (https://sites.google.com/view/animal-pose/)

AwA Pose Dataset (vaca/oveja/caballo/cerdo) (https://github.com/prinik/AwA-Pose)


Buscadores

Google Search Dataset (https://toolbox.google.com/datasetsearch)

Repositorio de la UCI para Machine Learning (http://mlr.cs.umass.edu/ml/)

Microsoft Research Open Data (https://msropendata.com/)

Buscador georreferenciado de datos abiertos (https://opendatainception.io/)

Portal de datos abiertos de la UE (http://data.europa.eu/euodp/es/data/)

Portal de datos abiertos de la FAO (http://www.fao.org/faostat/en/#data)

Gobierno de España datos abiertos (http://datos.gob.es/es)

Portal de datos abiertos Esri España (http://opendata.esri.es/)

Búsqueda de repositorios de datos abiertos (https://www.re3data.org/)


Modelos Preentrenados

Model Zoo (https://modelzoo.co/)

Tensorflow Model Garden (https://github.com/tensorflow/models)

Tensorflow Hub (https://tfhub.dev/)

Pytorch Hub (https://pytorch.org/hub/)

MediaPipe Models (https://google.github.io/mediapipe/solutions/models)

Piinto Model Zoo (https://github.com/PINTO0309/PINTO_model_zoo)

ONNX Model Zoo (https://github.com/onnx/models)

Jetson Zoo (https://www.elinux.org/Jetson_Zoo#Model_Zoo)

Awesome CoreML Models (iOS Apple) (https://github.com/likedan/Awesome-CoreML-Models)

Open Model Zoo (https://github.com/openvinotoolkit/open_model_zoo)

YOLO v3 y otros detectores (https://pjreddie.com/darknet/yolo/)

Kaggle Models (https://www.kaggle.com/models)


APIs

Listado de APIs de Google (https://developers.google.com/apis-explorer/#p/)

Listado de APIs de IBM (https://developer.ibm.com/api/list)

Listado de APIs de Microsoft (https://msdn.microsoft.com/en-us/library/ms123401.aspx)

Búscadores de APIs por nombre o temática (https://any-api.com/ ; http://apis.io/ ; https://apis.guru/browse-apis/)


Libros y documentos

Español

Aprender R: Iniciación y Perfeccionamiento (https://myrbooksp.netlify.app/)

Aprendizaje Automático (https://urjcdslab.github.io/AprendizajeAutomaticoI/)

AnalizaR Datos políticos (https://arcruz0.github.io/libroadp/index.html)

Econometría, Estadística y Machine Learning con R (https://econometria.wordpress.com/2017/07/23/estadistica-y-machine-learning-con-r/)

Estadística Aplicada a las Ciencias y la Ingeniería (https://emilopezcano.github.io/estadistica-ciencias-ingenieria/index.html)

Fundamentos de Ciencia de Datos con R (https://cdr-book.github.io/index.html)

Inferencia Estadística (https://urjcdslab.github.io/InferenciaEstadistica/)

Interpretable Machine Learning *Black Box (Spanish Edition) (https://fedefliguer.github.io/AAI/)

Introducción a estadística con R (https://bookdown.org/matiasandina/R-intro/)

Introducción a la ciencia de datos - Análisis de datos y algoritmos de predicción con R (https://rafalab.github.io/dslibro/)

Introducción a la Estadística para Científicos de Datos con R (https://analisisydecision.es/estadistica-data-scientist/index.html)

Introducción al Análisis de Datos con R (https://rubenfcasal.github.io/intror/)

Introducción al software estadístico R (https://www.lcano.com/b/iser/_book/index.html)

Libro vivo de Ciencia de Datos (https://librovivodecienciadedatos.ai/)

Manual de R (https://fhernanb.github.io/Manual-de-R/)

Modelado Ordenado con R (https://davidrsch.github.io/TMwRes/)

Modelos Estadísticos Avanzados (https://pegasus.uprm.edu/~pedro.torres/book/)

Modelos estadísticos con R (https://bookdown.org/j_morales/weblinmod/)

Modelos lineales y aditivos en ecología (https://bookdown.org/fxpalacio/bookdown_curso/)

Modelos Predictivos (https://fhernanb.github.io/libro_mod_pred/)

R Avanzado (https://davidrsch.github.io/adv-res/)

R para Ciencia de Datos (1º edición: https://es.r4ds.hadley.nz/; 2º edición: https://davidrsch.github.io/r4dses/)

R para principiantes (https://bookdown.org/jboscomendoza/r-principiantes4/)

R para profesionales (https://www.datanalytics.com/libro_r/)


Inglés

Apache Arrow R Cookbook (https://arrow.apache.org/cookbook/r/index.html)

Behavior Analysis with Machine Learning Using R (https://enriquegit.github.io/behavior-free/index.html)

Big Book of R (libro recopilatorio de libros) (https://www.bigbookofr.com/)

Caret Package R (http://topepo.github.io/caret/index.html)

Causal Inference in R (https://www.r-causal.org/)

Data Science in Education using R (https://datascienceineducation.com/)

Data Science Live Book R (https://livebook.datascienceheroes.com/)

Deep Learning MIT Press book (https://www.deeplearningbook.org/)

Deep Learning on Graphs (http://cse.msu.edu/~mayao4/dlg_book/)

Dive into Deep Learning (https://d2l.ai/)

Elegant and informative maps with tmap (https://r-tmap.github.io/tmap-book/)

Elegant Graphics for Data Analysis with ggplot2 (https://ggplot2-book.org/index.html)

Engineering Production-Grade Shiny Apps (https://engineering-shiny.org/)

Explanatory Model Analysis (https://ema.drwhy.ai/)

Feature Engineering A-Z (https://feaz-book.com/)

Feature Engineering and Selection (http://www.feat.engineering/)

Forecasting and Analytics with ADAM (https://openforecast.org/adam/)

Forecasting: Principles and Practice (R) (Second Edition: https://otexts.org/fpp2/; Third Edition: https://otexts.org/fpp3/)

Fundamentals of Data Visualization (https://clauswilke.com/dataviz/index.html)

Geocomputation with Python (https://py.geocompx.org/)

Geocomputation with R (https://geocompr.robinlovelace.net/)

Graph Representation Learning Book (https://www.cs.mcgill.ca/~wlh/grl_book/)

Interactive web-based data visualization with R, plotly, and shiny (https://plotly-r.com/index.html)

Interpretable Machine Learning Black Box (https://christophm.github.io/interpretable-ml-book/)

Learning Statistical Models Through Simulation in R (https://psyteachr.github.io/stat-models-v1/index.html)

Limitations of Interpretable Machine Learning (https://compstat-lmu.github.io/iml_methods_limitations/)

Machine Learning Engineering Book (http://www.mlebook.com/wiki/doku.php)

Mastering Spark with R (https://therinspark.com/)

Mastering Shiny (https://mastering-shiny.org/)

Modern R with the tidyverse (https://b-rodrigues.github.io/modern_R/)

Natural Language Processing with Python (https://www.nltk.org/book/)

Outstanding User Interfaces with Shiny (https://unleash-shiny.rinterface.com/index.html)

R Base Graphics (http://rstudio-pubs-static.s3.amazonaws.com/7953_4e3efd5b9415444ca065b1167862c349.html)

R Graphics Cookbook (https://r-graphics.org/)

R Markdown: The Definitive Guide (https://bookdown.org/yihui/rmarkdown/)

Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition (https://bookdown.org/content/4857/)

Supervised Machine Learning for Science (https://ml-science-book.com/)

The Data Engineering Cookbook (https://github.com/andkret/Cookbook)

The Hundred-Page Machine Learning Book (http://themlbook.com/wiki/doku.php)

Tidy Modeling with R (https://www.tmwr.org/)

UC Business Analytics R Programming Guide (http://uc-r.github.io/)

YaRrr! The Pirate’s Guide to R (https://bookdown.org/ndphillips/YaRrr/)


Blogs

Español

Ciencia y datos (https://medium.com/datos-y-ciencia; https://www.cienciaydatos.org/)

Materiales de RStudio en Español (https://resources.rstudio.com/espanol)

Joaquin Amat R (https://rpubs.com/Joaquin_AR)

Ciencia de Datos, Estadística, Visualización y Machine Learning (https://www.cienciadedatos.net/)

Escuela de Datos Vivos (https://blog.escueladedatosvivos.ai/)

Machine Learning para todos (http://machinelearningparatodos.com/blog/)

Foro argentino de R (https://datosenr.org/)

Tutoriales R & Python (https://datascienceplus.com/)

LUCA Telefónica (https://data-speaks.luca-d3.com/)

Análisis de datos y Machine Learning-dlegorreta (https://dlegorreta.wordpress.com/)

Aprender machine learning (http://www.aprendemachinelearning.com/)

BI y Machine Learning con Diego Calvo (http://www.diegocalvo.es/)

Ejemplos de Machine Learning y Data Mining con R (http://apuntes-r.blogspot.com/)

Aprendiendo sobre IA-Lidgi González (http://ligdigonzalez.com/)

Actualidad de Big data e Inteligencia artificial (http://www.sorayapaniagua.com/)

Blog sobre python (http://www.pythondiario.com/)

Visión por computador (https://carlosjuliopardoblog.wordpress.com/)

DataSmarts (https://datasmarts.net/es/)

OMES, Visión por computador (https://omes-va.com/)

Inteligencia artificial en la práctica (https://iartificial.net/)

Tutoriales sobre ML y librerías usadas en ML (https://www.interactivechaos.com/tutoriales)

Blog con información sobre big data, aprendizaje automático e ia (http://sitiobigdata.com/)

Blog sobre fundamentos de ML e IA (https://www.juanbarrios.com/machine-learning-aprendizaje-maquina/)

Todo AI (https://todoia.es/)

Dream Learning blog (https://dreamlearning.ai/blog/)

Repositorio de cursos con diapositivas de IA, ML, DL - Fernando Berzal (https://elvex.ugr.es/courses.html)

Blog del profesor Fernando Sancho Caparrini (ETSI) sobre IA, ML, DL (http://www.cs.us.es/~fsancho/)

Blog del profesor Jordi Torres (https://torres.ai/blog/)

Blogg del profesor Eduardo Morales (https://ccc.inaoep.mx/~emorales/)

Escuela de Datos Vivos (https://escueladedatosvivos.ai/blog)

Blog Rubiales Alberto Medium (https://rubialesalberto.medium.com/)

Blog Javi GG (https://javi897.github.io/)

Blog Ander Fernández (https://anderfernandez.com/blog/)

Aprender Big Data (https://aprenderbigdata.com/)

Hablando en Data (https://hablandoendata.com/)

Análisis y decisión (https://analisisydecision.es/)

Mis apuntes Data Science (MDS) (https://misapuntesdedatascience.es/)

Hablamos R (https://hablamosr.blogspot.com/)

Inglés

ClaoudML (https://www.claoudml.com/)

Listen Data (https://www.listendata.com/)

Data Science Heroes (https://blog.datascienceheroes.com/)

Machine Learning para dispositivos móviles (https://heartbeat.fritz.ai/)

Tutoriales sobre aprendizaje automático en R y Python (https://www.machinelearningplus.com/)

Visión artificial con OpenCV (https://www.learnopencv.com/; github: https://github.com/spmallick/learnopencv/blob/master/README.md?ck_subscriber_id=323792569)

CV-Tricks Visión artificial (https://cv-tricks.com/)

Business Intelligence y Data Science (http://www.dataprix.com/)

Towards AI (https://medium.com/towards-artificial-intelligence)

Data Science Dojo (https://blog.datasciencedojo.com/)

Machine Learning Mastery (https://machinelearningmastery.com/)

Open Data Science (https://opendatascience.com/)

365 Data Science Blog (https://365datascience.com/blog/)

PyImageSearch (https://www.pyimagesearch.com/)

deepwizAI (https://www.deepwizai.com/)

ML in Production (http://mlinproduction.com/)

R in Production (https://www.rinproduction.com/en/)


Canales de Youtube

Dot CSV (https://www.youtube.com/channel/UCy5znSnfMsDwaLlROnZ7Qbg)

Lidgi González (https://www.youtube.com/channel/UCLJV54sFqPiH4MYcJKvGesg)

Descubriendo la inteligencia artificial (https://www.youtube.com/channel/UCrEM9nM7pxy0TtgDyTXljFQ)

Xpikuos - ML / IA / Robótica (https://www.youtube.com/channel/UCCmHFfUhcgZHenBWRzSEB0w/featured)

Luca Talks (https://www.youtube.com/channel/UCiz4K2MbbIEAr31L3Wps3Ew/featured)

cctmexico (https://www.youtube.com/playlist?list=PLgHCrivozIb0HQ9oPRLVqw5scdIG-AxQL)

AMP Tech (https://www.youtube.com/playlist?list=PLA050nq-BHwMr0uk7pPJUqRgKRRGhdvKb)

Luis Serrano (https://www.youtube.com/playlist?list=PLs8w1Cdi-zvZ43xD_AA-eAuEW1FLK0cef)

Capacítate para el empleo (https://www.youtube.com/watch?v=kKm1cXSyLqk)

Full Stack Deep Learning (https://www.youtube.com/c/FullStackDeepLearning/featured)

Applied Machine Learning 2020 (https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)

Statistical Machine Learning (https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC)

Advanced Deep Learning & Reinforcement Learning (https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)

Data Driven Control with Machine Learning (https://www.youtube.com/playlist?list=PLMrJAkhIeNNQkv98vuPjO2X2qJO_UPeWR)

ECE AI Seminar Series (https://www.youtube.com/playlist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U)

CSEP 546 - Machine Learning (https://www.youtube.com/playlist?list=PLTPQEx-31JXj87XLsYutYGKw6K9dNaD36)

CS285 Fall 2019 (https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A)

Deep Bayes (https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW)

CMU Neural Nets for NLP (https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ)

Workshop on New Directions in Reinforcement Learning and Con (https://www.youtube.com/playlist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3)

Theoretical Machine Leraning Lecture Series (https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5VLprf2VUfC0h1zOGvV_gz)


Plataformas de aprendizaje

Coursera (https://www.coursera.org/)

edX (https://www.edx.org/es)

miriadaX (https://miriadax.net/home)

Udemy (https://www.udemy.com/)

Udacity (https://eu.udacity.com/)

SimpliLearn (https://www.simplilearn.com/)

FutureLearn (https://www.futurelearn.com/)

freeCodecamp (https://www.freecodecamp.org/)

Codecademy (https://www.codecademy.com/)

Cursos python en español (https://unipython.com/)

Katacoda (www.katacoda.com)

365 Data Science (https://365datascience.com/)

Linux Academy (https://linuxacademy.com/)

PluralSight (www.pluralsight.com)

Intellipaat (https://intellipaat.com/)


Otros programas/recursos

Papers with Code (https://paperswithcode.com/)

Google App Maker (https://blog.google/outreach-initiatives/education/build-custom-apps-your-school-app-maker/)

Cisco Academy Cursos (https://www.netacad.com/es/courses/all-courses)

Estado del arte de la IA (https://www.stateoftheart.ai/)

Generar webs estáticas con Rmarkdown (https://github.com/rstudio/blogdown)

Escribir libros con Rmarkdown (https://github.com/rstudio/bookdown)

Certificaciones Data Science (https://digitaldefynd.com/best-data-science-certification-course-tutorial/)

Diccionario tecnológico IA & Big Data (https://luca-d3.com/es/diccionario-tecnologico/index.html)

Aprendizaje interactivo de Probabilidad básica (https://seeing-theory.brown.edu/)

Google Talk to Books (https://books.google.com/talktobooks/)

Búsqueda repositorios Github (https://gitdiscoverer.shinyapps.io/rstudio-shiny-contest/)

Asociación Española para la Inteligencia Artificial (AEPIA) (http://www.aepia.org/aepia/index.php)

Ayuda interactiva para elección de gráficos (https://www.data-to-viz.com/index.html)

Introducción visual al aprendizaje automático (http://www.r2d3.us/)

Creación de infografías (Infogram, Piktochart, Canvas) (https://infogram.com/; https://piktochart.com/; https://www.canva.com/es_es/)

Presentaciones interactivas (https://www.genial.ly/es)

Galería de gráficos en R (https://www.r-graph-gallery.com/)

Galería de gráficos en Python (https://www.python-graph-gallery.com/)

Página web sobre estadística con R (http://www.sthda.com/english/)


Paquetes R

Importación

readr, readxl, XML, jsonlite, httr, DBI

Exploración

DataExplorer, GGally, summarytools, skimr, funModeling, radiant, anomalize, correlationfunnel, corrplot

Limpieza y manipulación

tidyr, dplyr, dbplyr, data.table, dtplyr (data.table), datapasta, forcats, janitor, lubridate, stringr, purrr, drake (pipelines)

Visualización

ggplot2, plotly, ggstatsplot (jjstatsplot con GUI), trelliscopejs, esquisse, ggplotgui, gganimate, ggforce, rayshader, r2d3

Geoespaciales

sen2r, rgee, ggmap, gstat, spatstat, sf, sp, raster, rgdal, leaflet, RQGIS3, tmap, rgeos, whiteboxR

Estadística y Machine learning/Deep Learning

infer, rstatix, tidymodels, stacks, caret, mlr, h2o, glmnet, tensorflow, keras, ruta (unsupervised DL), xgboost, lightgbm, parsnip, recipes, recommenderlab

Series temporales

forecast, fable, feasts, timetk, maltese, modeltime, modeltime.ensemble, modeltime.gluonts, prophet

Procesamiento del lenguaje natural

tidytext, text2vec, quanteda

Interpretabilidad de modelos

iml, DALEX, LIME, shapr, modelStudio (interactive dashboard), DrWhy.AI, modelDown

Paralelización y Big Data

parallel, foreach, bigmemory, bigtabulate, biganalytics, iotools, sparklyr, rsparkling, furrr

Despliegue

plumber, rdocker, cloudml, cloudyr, aws.s3, Paws (AWS), AzureR (familia paquetes)

Reporting

knitr, rmarkdown, shiny, flexdashboard, shinydashboard, shinymanager, Microsoft365R

WebScrapping

rvest, RSelenium

Otros

reticulate, pdftools, tabulizer, tesseract, utils, onnx, aurelius, ArenaR, fs, fusen


Paquetes Python

Importación

SQLAlchemy, pandas, PyMongo

Exploración

pandas, bamboolib, pandas_profiling, D-Tale, pandasgui, pandas_ui, sweetviz, funpymodeling, autoplotter, lux, mito, QuickDA

Limpieza y manipulación

pandas, numpy, scipy, featuretools, feature-engine, featurewiz, siuba (R dplyr syntax), kangas (pandas for computer vision)

Automatización

PyAutoGUI, AutoPy, rpa, rpaframework, watchdog

Visualización

seaborn, bokeh, matplotlib, plotly, plotnine (R ggplot syntax)

Geoespaciales

GeoPandas, PyQGIS, GDAL, Folium, ipyleaflet, geemap, WhiteboxTools, leafmap

Estadística y Machine Learning/Deep Learning

statsmodels, scikit-learn, imbalanced-learn, PyOD, pycaret, Keras, Tensorflow, PyTorch, skorch (sklearn + pytorch), xgboost, ngboost, Hyperopt, scikit-optimize (skopt), DEAP, TPOT

Series temporales

statsmodels.tsa, Darts, sktime, skforecast, Kats, AutoTS, tslearn, tsfresh, fbprophet, GluonTS, neuralprophet, tsai, nixtla

Procesamiento del lenguaje natural

NLTK, Gensim, spaCy, CoreNLP, TextBlob, polyplot

Aprendizaje por refuerzo

Tensorforce, TFAgents, RLlib, Stable Baselines, RL_Coach, Coax

Interpretabilidad de modelos

yellowbrick, LIME, ELI5, MLxtend, Shapash, DrWhy.AI

Paralelización y Big Data

Dask, Vaex, modin, PySpark, optimus, koalas, polars

Despliegue

boto3 (AWS), BentoML, FastAPI, Flask

Reporting

dash, streamlit, stlite (Serverless Streamlit), gradio, panel, voilà, PyWebIO, mia, taipy, shinyexpress

Monitoring/Drifting

frouros, alibi-detect, evidently

WebScrapping

BeautifulSoup, scrapy, selenium

GUIs

Gooey, PySimpleGUI, PyGTK, wxPython, PyQT, Tkinter, PySide2

Otros

onnx


Casos de uso

Recomendaciones personalizadas, análisis social media, predicción de ventas, mantenimiento predictivo, automatización de procesos, detección de fraudes, análisis financiero, servicios asistidos de atención al cliente, procesamiento del lenguaje natural, tratamiento de salud personalizado, traducción, audio y voz, etc.


Automated machine learning

LazyPredict (https://lazypredict.readthedocs.io/en/latest/index.html)

AutoML (https://cloud.google.com/automl/?hl=es-419)

Auto-Keras (https://autokeras.com/)

Auto-Sklearn (https://automl.github.io/auto-sklearn/master/)

Auto-Weka (https://www.cs.ubc.ca/labs/beta/Projects/autoweka/)

AutoGluon (https://autogluon.mxnet.io/)

AutoGOAL (https://autogoal.github.io/)

MLBox (https://mlbox.readthedocs.io/en/latest/)

TPOT (https://github.com/EpistasisLab/tpot)

H2O AutoML (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html)

TransmogrifAI (https://transmogrif.ai/)

Glaucus (https://github.com/ccnt-glaucus/glaucus)

EvalML (https://github.com/alteryx/evalml)


Embedded Machine Learning

Optimización de modelos para dipositivos embebidos (edge devices) (arduino, raspberry pi, Jetson Nano, ESP32, móviles....)


Spatial Data Science / Machine Learning

Libros


Herramientas de Anotación