LQuartz27

LQuartz27

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LQuartz27's repositories

15-minute-apps

15 minute (small) desktop apps built with PyQt

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202104

Algoritmos de Clasificación

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box-plots-sklearn

An implementation of some of the tools used by the winner of the box plots competition using scikit-learn.

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clean-code-python-es

Guía en español: Clean Code Python

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ColorRef

La famosa teoría del color 🎨 llevada a código para ayudar a simplificar las elecciones del artista 👩‍🎨 dándole opciones y sugerencias sobre que Colores usar en su trabajo 🖌

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curso-webscrapping-con-xpath-

curso webscrapping con xpath by platzi

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Proyecto-Cafeteria-Brioche

Proyecto para: Ciclo de desarrollo de software - MisionTIC2022

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deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

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Django-3-by-Example

Django 3 by Example, published by Packt

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Intelligent-Projects-Using-Python

Intelligent Projects Using Python, published by Packt

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Learn-Data-Structures-and-Algorithms-with-Golang

Learn Data Structures and Algorithms with Golang, published by Packt

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MisionTIC2022

MisionTIC2022 Repository

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ML_SageMaker_Studies

Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.

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nare-app

Web App utilizada para ejecutar funcionalidades soporte para la homologación de pozos. Proyecto NARE

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notes-on-causal-inference

Some notes on Causal Inference, with examples in python

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platzi-mongo

En este repositorio se encuentra el código del proyecto platzi-mongo.

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practical-docker-with-python

Source Code for 'Practical Docker with Python' by Sathyajith Bhat

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practical-statistics-for-data-scientists

Code repository for O'Reilly book

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public-apis

A collective list of free APIs for use in software and web development.

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pwptemp

pwptemp

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seaborn-data

Data repository for seaborn examples

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The-Complete-Python-and-PostgreSQL-Developer-Course

The Complete Python and PostgreSQL Developer Course, published by Packt

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Time-Series-EDA-and-Forecast

In this section, I begin with the excel file of sales data, which I obtained from the Tableau Community Forum. As a recall, the data contains mostly categorical variables and components of the vectors from the description column. The index column is a timeseries format. The major objective of this section is to understand the general trends in the data, and gain some quick insights, and then predict and forcast the Sales of the category "Technology" of the given sales data.The statistical significance of these observations will be also tested in 'Exploratory Data Analysis'.

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web-apis-with-python

Code repository for exercises in the eBook WebAPI Development with Python

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