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En este proyecto guiado, desarrollarás un ambiente MERN usando Linux UBUNTU desde cero. Construiremos el Frontend y el Backend de una aplicación Fullstack. Al finalizarla, serás capaz de comenzar a desarrollar una aplicación.
Proyecto aplicado y práctico para aprender a entrenar modelos de Machine Learning como: AR, MA, ARMA, ARIMA, autoARIMA, SARIMA y autoSARIMA para predecir series temporales con Python.
In this project you will learn how to set up PyCaret for your natural language processing tasks, compare and create models effectively, visualize your models and corpus. All this with just a few lines of code.
In this 2-hour long project-based course, you will learn how to interpret the dataset for machine learning, how different features impact on a mode and how to evaluate them.
Build a facial keypoint detector in PyTorch: write a custom image-keypoint dataset class, augment keypoints with albumentations, load a pretrained network via timm, and train it.
Build an end-to-end regression model with PyCaret to predict concrete strength: prepare and visualize data, create a transformation pipeline, then compare, interpret and deploy models.
Manage a large COVID-19 dataset with SQL in MySQL Workbench: pick the right database engine, index tables, create summary tables, and write efficient queries.
Mit Canva ansprechende Story Posts für Instagram und Facebook gestalten, um ein Business mit kleinem Marketingbudget visuell zu bewerben.
Erstelle mit der kostenlosen Canva-Version ein visuelles Storyboard, das Handlungsverläufe strukturiert und als Vorlage für zukünftige Projekte dient.
En este proyecto guiado, desarrollaremos la integración de Facebook Pixel con una tienda de Shopify desde cero. Está dirigido a profesionales de Marketing y entusiastas en conocer la plataforma de Facebook Pixel para analizar el tráfico de usuarios.
Deploy a web application that uses the AWS SDK and Amazon Rekognition to upload and index videos, detecting faces, objects, labels, celebrities, and mature content.
Proyecto práctico para entrenar modelos de predicción de series temporales con PyCaret y Python, incluidos XGBoost, CatBoost, Random Forest y técnicas de ensamblado.
Apprenez à utiliser Canva pour créer et personnaliser des visuels graphiques attrayants pour vos publications sur les réseaux sociaux, sans connaissances préalables en graphisme.
Build a low-fidelity responsive website prototype in Adobe XD, focusing on workflow, content, and layouts that adapt across a range of device screen sizes.
Build a low-fidelity mobile app prototype in Adobe XD: add screens, control flow and transitions, place text labels, toggle buttons and images, and make reusable components.
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