#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

Overview

30 Days Of Streamlit 🎈

This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

How to participate

All you need to participate is a computer, a basic understanding of Python, and your curiosity. 🧠

A new challenge is released daily via Streamlit's Twitter and LinkedIn accounts as well as the #30DaysOfStreamlit app.

Streamlit App

Complete the daily challenges, share your solutions with us on Twitter or LinkedIn, and get rewarded with cool Streamlit swag! 😎

What are the daily challenges?

Find out more about the specific challenges by participating! The 30-day challenges are divided by 3 levels of difficulty to appeal to participants of all skill levels:

Beginner (Days 1-7) Intermediate (Days 8-23) Advanced (Days 24-30)
Set up your local and cloud coding environments, install Streamlit, and build your first Streamlit app. Learn about a new Streamlit command each day and use it to create and deploy a simple Streamlit app. Learn about important topics such as session state, efficient data and memory management via caching, complex layouts, and much more.

Prizes

If getting up to speed with the fastest and easiest way to build data apps isn't already the best summer gift, you can also win Streamlit goodies!

Complete the daily challenges, share your solutions with us on Twitter or LinkedIn, and get rewarded with cool Streamlit swag! 🎁

Resources

Translations

Want to help us expand the reach of #30DaysOfStreamlit and English isn't your primary language? Translate the challenges into your preferred language and link to them below!

Owner
Streamlit
The fastest way to build custom ML tools
Streamlit
Nixtla is an open-source time series forecasting library.

Nixtla Nixtla is an open-source time series forecasting library. We are helping data scientists and developers to have access to open source state-of-

Nixtla 401 Jan 08, 2023
Machine Learning Study 혼자 해보기

Machine Learning Study 혼자 해보기 기여자 (Contributors) ✨ Teddy Lee 🏠 HongJaeKwon 🏠 Seungwoo Han 🏠 Tae Heon Kim 🏠 Steve Kwon 🏠 SW Song 🏠 K1A2 🏠 Wooil

Teddy Lee 1.7k Jan 01, 2023
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

Spark Python Notebooks This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, fro

Jose A Dianes 1.5k Jan 02, 2023
Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan

Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.

Abid Ali Awan 1 Dec 31, 2021
PyPOTS - A Python Toolbox for Data Mining on Partially-Observed Time Series

A python toolbox/library for data mining on partially-observed time series, supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing va

Wenjie Du 179 Dec 31, 2022
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models. Solve a variety of tasks with pre-trained models or finetune them in

Backprop 227 Dec 10, 2022
BudouX is the successor to Budou, the machine learning powered line break organizer tool.

BudouX Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning powered line break organizer tool. It is standalone

Google 868 Jan 05, 2023
Factorization machines in python

Factorization Machines in Python This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive re

Corey Lynch 892 Jan 03, 2023
Production Grade Machine Learning Service

This project is made to help you scale from a basic Machine Learning project for research purposes to a production grade Machine Learning web service

Abdullah Zaiter 10 Apr 04, 2022
Machine Learning for Time-Series with Python.Published by Packt

Machine-Learning-for-Time-Series-with-Python Become proficient in deriving insights from time-series data and analyzing a model’s performance Links Am

Packt 124 Dec 28, 2022
Evaluate on three different ML model for feature selection using Breast cancer data.

Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.

Tarek idrees 1 Mar 17, 2022
MIT-Machine Learning with Python–From Linear Models to Deep Learning

MIT-Machine Learning with Python–From Linear Models to Deep Learning | One of the 5 courses in MIT MicroMasters in Statistics & Data Science Welcome t

2 Aug 23, 2022
XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

92 Dec 14, 2022
A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Currently a Beta-Version lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning mod

lucidmode 15 Aug 12, 2022
A Python toolkit for rule-based/unsupervised anomaly detection in time series

Anomaly Detection Toolkit (ADTK) Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. As

Arundo Analytics 888 Dec 30, 2022
Generate music from midi files using BPE and markov model

Generate music from midi files using BPE and markov model

Aditya Khadilkar 37 Oct 24, 2022
Repositório para o #alurachallengedatascience1

1° Challenge de Dados - Alura A Alura Voz é uma empresa de telecomunicação que nos contratou para atuar como cientistas de dados na equipe de vendas.

Sthe Monica 16 Nov 10, 2022
Simple data balancing baselines for worst-group-accuracy benchmarks.

BalancingGroups Code to replicate the experimental results from Simple data balancing baselines achieve competitive worst-group-accuracy. Replicating

Facebook Research 29 Dec 02, 2022
A unified framework for machine learning with time series

Welcome to sktime A unified framework for machine learning with time series We provide specialized time series algorithms and scikit-learn compatible

The Alan Turing Institute 6k Jan 06, 2023
MLOps pipeline project using Amazon SageMaker Pipelines

This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drif

AWS Samples 3 Sep 16, 2022