LOGiN PANeL

«    November 2026    »
MoTuWeThFrSaSu
 1
2345678
9101112131415
16171819202122
23242526272829
30 
PoLL





eBooks Tutorials Templates Plugins Scripts Applications GFX Collections SCRiPTMAFiA.ORG
LaST oN NULLeD.org
The Whole Truth (1958) 1080p BluRay-LAMA 1.4 GB | 01:24:23 | mp4 | 1888X1080 | 16:9 Language:English Genres: Mystery,Thriller iMDB info ...
Magic Cutter Pro - MP3 Editor 2.1 macOS File size: 10.5 MB Magic Cutter offer a quick way to automatically cut audio file by file size, pieces count, time ...
Ghost Buster Pro 7.1.1 macOS File Size: 7.73 MB Ghost Buster Pro finds and deletes files from apps you've already uninstalled. The app is lightning ...

RSS
RSS

FRiENDS
Nulled.org Software 8TM URL Shortener Forex Trading Crypto Trading {sape_teaser}



Machine Learning Model Deployment with Streamlit

Category: Tutorials


Machine Learning Model Deployment with Streamlit
Published 9/2023
Created by Marco Peixeiro
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 44 Lectures ( 7h 13m ) | Size: 3 GB
Deploy ML models with Streamlit and share your data science work with the world


Free Download What you'll learn
Understand the core concepts and features of Streamlit
Build interactive data-driven web applications to deploy your model
Master the advanced features and integrations in Streamlit
Apply the best practices and optimization techniques for Streamlit
Connect your Streamlit app to data sources
Deploy your Streamlit app for free
Requirements
A working knowledge of Python and machine learning is required.
This course focuses only on deploying models using Streamlit. We will not spend time explaining how the models work or how they are developed and trained.
A computer with Anaconda installed.
Your favourite text editor installed (I use VSCode)
Description
The complete course to deploy machine learning models using Streamlit. Build web applications powered by ML and AI and deploy them to share them with the world.This course will take you from the basics to deploying scalable applications powered by machine learning. To put your knowledge to the test, I have designed more than six capstone projects with full guided solutions.This course covers:Basics of StreamlitAdd interactive elements, like buttons, forms, sliders, input elements, etc.Display chartsCustomize the layout of your application Capstone project: build an interactive dashboardCachingPerformance enhancement with cachingBasic and advanced usage of cachingCapstone project: deploy a classification modelSession state managementAdd more interactivity and boost performance with session state managementBasic and advanced usage of session stateCapstone project: deploy a regression modelMultipage applicationsBuild large apps with multiple pagesCapstone project: train and rank classification modelsAuthenticationAdd a security layer with authenticationAdd login/logout componentsAdvanced authentication with user management, reset password, etc.Capstone project: deploy a clustering model for marketingConnect to data sourcesConnect to databasesAccess data through APIsCapstone project: Deploy a sales demand modelDeploymentDeploy a Streamlit app for freeAdvanced deployment process with secrets management and environment variables
Who this course is for
Data scientists and machine learning engineers looking to deploy ML models and dashboards.
Homepage
https://www.udemy.com/course/machine-learning-model-deployment-with-streamlit/




DOWNLOAD LINK


No Password - Links are Interchangeable


   
   
   


Forex Trading

Crypto Trading. 20 USD Bonus after KYC




We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm





 

Information

 
  Users of GUESTS are not allowed to comment this publication.