Pinned Repositories
scikit-lego
Extra blocks for scikit-learn pipelines.
aws_conda_environments_scripts
AWS Conda Environment Scripts
cpython
The Python programming language
disaster-response-pipeline
Machine learning pipeline to categorize real messages that were sent during disaster events
stackoverflow
Findings from Stackoverflow 2017
streamlit_plant_disease_classification_demo
Streamlit Plant Disease Classification Demo
video-game-sales-analysis
Video game sales analysis
tomasborrella's Repositories
tomasborrella/aws_conda_environments_scripts
AWS Conda Environment Scripts
tomasborrella/streamlit_plant_disease_classification_demo
Streamlit Plant Disease Classification Demo
tomasborrella/video-game-sales-analysis
Video game sales analysis
tomasborrella/cpython
The Python programming language
tomasborrella/disaster-response-pipeline
Machine learning pipeline to categorize real messages that were sent during disaster events
tomasborrella/stackoverflow
Findings from Stackoverflow 2017
tomasborrella/datasharing
The Leek group guide to data sharing
tomasborrella/DSND_Term2
Contains files related to content and project of DSND Term 2
tomasborrella/ml-engineer-nanodegree-starbucks-capstone
tomasborrella/ML_SageMaker_Studies
Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
tomasborrella/proy1
tomasborrella/proy2_2024
tomasborrella/proyecto2
tomasborrella/pydrift
How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to predict. `pydrift` tries to facilitate this task to the data scientist, performing this kind of checks and somehow measuring that degradation.
tomasborrella/sagemaker-deployment
Code and associated files for the deploying ML models within AWS SageMaker
tomasborrella/scikit-learn
scikit-learn: machine learning in Python
tomasborrella/scikit-lego
extra blocks for sklearn pipelines
tomasborrella/spark_test_1
tomasborrella/streamlit1_live_demo
tomasborrella/streamlit_ejemplo_1
Streamlit sample app
tomasborrella/streamlit_huggingface_transformers_demo
Streamlit Hugging Face Transformers Demo
tomasborrella/streamlit_titanic_demo
Streamlit Titanic Demo
tomasborrella/TheValley
Material de las sesiones impartidas en el MDS+ de The Valley.
tomasborrella/tomasborrella.github.io