Standup Bot
Sup! Standup Bot
Standuply
Geekbot
Tatsu
MeetNotes
Chili Piper
Doodle
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
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Exploratory
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StandupBot is an easy to use bot that automates your team’s standups, check-ins or any kind of recurring status update meetings, without breaking the bank. Trusted by thousands of teams to run over a million standups in our 8+ year history.
Unlike other tools that try to do way too things and are super confusing to manage, we focus on what you really need to automate your team’s meetings:
⚡️Fast setup: From install to first meeting in under 60 seconds. Great defaults to get you going and super easy to change to your needs.
👥 Multiple teams and projects: Create as many standups or status meetings you need for different projects or teams.
🕘 100% asynchronous: Everyone participates when it’s more convenient for them.
📃 Standup Report: Receive an easy-to-read report via email and Slack when the meeting is done.
👀 “Just following” mode: Select who's actively participating in meetings and who's only following through reports.
📆 Flexible scheduling: Schedule your meetings at the days and times you need. Automatically excuse people from meetings when they’re on vacation.
✅ Participation reports: Team- and individual-level participation reports, so you can easily see who needs some encouragement to share their updates more frequently.
🔔 Automatic reminders: We’ll be the friendly drill-sergeant for your team reminding everyone that hasn’t submitted their standup to do so before the meeting window closes.
Standup Bot Scikit-learnNo features have been listed yet.
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Based on our record, Scikit-learn seems to be more popular. It has been mentiond 29 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 days ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 4 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Sup! Standup Bot - The complete stand-up and follow-up bot
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Standuply - Run daily standup meetings and track your metrics in Slack
OpenCV - OpenCV is the world's biggest computer vision library
Geekbot - Discover how to organise asynchronous stand up meetings in Slack and keep your team synced using Geekbot. Start your free trial today!
NumPy - NumPy is the fundamental package for scientific computing with Python