Machine learning fundamentals
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Updated
May 17, 2024 - Jupyter Notebook
Machine learning fundamentals
Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.
This is a repository to learn and get more computer vision skills, make robotics projects integrating the computer vision as a perception tool and create a lot of awesome advanced controllers for the robots of the future.
Thesis project: topic categorization and sentiment analysis on twitter with Apache Spark
Machine Learning and Deep Learning Tutorial
Building a Machine Learning Library from scratch using Python3, based on SOTA library Scikit-learn
A Deep Learning based Fashion Recommendation System using the ResNET50
Free High-Quality Financial Data in Azure
Machine Learning Projects in Python. Examples of popular machine learning algorithms with interactive Jupyter code explained
A shoe👟 recommendation website.
Custom classifiers to detect sexist language.
My Python learning experience 📚🖥📳📴💻🖱✏
This is the framework for supervised algorithms in mechine learning
Repositorio del Training realizado por Factored. "Aprender como entrenar y desplegar modelos de ML" 📈🐍
This repo attempts to utilise two powerful ensemble models, Random forest and Gradient Boosting to Predict the failure patterns of wind energy machinery
LABS Proyecto Individual: MVP Steam - Rol: MLOps Engineer | Bootcamp Henry: Carrera Data Science | Cohorte DataFT 17
We use our customer geolocation data to perform a clustering algorithm to get several clusters in which the member data of each cluster are closest to each other using KMeans and Constrained-KMeans Algorithms.
A python code to training your own spam filter in Python
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