studio
100-Days-Of-ML-Code
| studio | 100-Days-Of-ML-Code | |
|---|---|---|
| 3 | 3 | |
| 4 | 43,337 | |
| - | - | |
| 3.2 | 0.0 | |
| over 2 years ago | 4 months ago | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
studio
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Show HN: Mljar Studio visual programming for Python Notebook
With my wife, we are working on visual interface for creating Python scripts in the notebook. We created desktop application MLJAR Studio. In our app, user has a list of predefined steps. Each step has a graphical interface with a form that after filling generate the Python code. The Python code is the source of the truth.
Currently we have a few steps for training Machine Learning model on tabular data. [Here you have few gifs with screenshots](https://mljar.com/docs/how-does-python-notebook-work/) how it looks like, and [example how to build ML model](https://mljar.com/docs/create-first-notebook/) on tabular data. The created notebook is compatible with Jupiter notebook.
In the near future, we are planning to add notebook scheduling and more steps (probably with some dynamic manager for steps loading). We see MLJAR Studio as an alternative to visual programming environments which are node based. Because the Python code is the source of truth, it offers a great flexibility to define new steps or to add custom Python code.
The app is desktop based (it is using electron framework). It automatically installs Python 3.9 with miniconda and required packages. The installation is local, without change to the environment path. You can see installation instructions [here](https://mljar.com/docs/install-notebook/). The application is only for Windows. If you are interested in MacOS or Linux versions, please fill the [form](https://docs.google.com/forms/d/e/1FAIpQLSeB5-hA326sBg9fg-pp...) and we will notify you when ready.
If you would like to try the app (currently Windows only), it can be downloaded from GitHub release page: https://github.com/mljar/studio/releases
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I'm working on visual programming for Python notebooks - alternative for node-based programming environments
If you would like to try the app (currently Windows only), it can be downloaded from GitHub release page: https://github.com/mljar/studio/releases
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[D] Bring your own data AI SaaS service for non-programmers?
Instead, we started to work on desktop application that will allow to create python notebooks with no-code GUI (https://github.com/mljar/studio some screenshots on our website ).
100-Days-Of-ML-Code
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Top 10 GitHub Repositories Every Developer Should Bookmark in 2024
2) 100 Days of ML Code: Embark on a 100-day journey into the fascinating world of machine learning with this structured curriculum. Packed with bite-sized coding challenges and real-world projects, this repository will transform you from a coding novice to a confident ML enthusiast. (https://github.com/Avik-Jain/100-Days-Of-ML-Code)
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✨ 5 Best GitHub Repositories to Learn Machine Learning in 2022 for Free 💯
1️⃣ 100 Days Of ML Code
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The Ultimate Resource Guide for Your Next 100 Days of Code
ML: 100-Days-Of-ML-Code
What are some alternatives?
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
100DaysofMLCode - My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
leetcode-master - 《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
machine_learning_basics - Plain python implementations of basic machine learning algorithms
Data-science-best-resources - Carefully curated resource links for data science in one place
machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer.
dive-into-machine-learning - Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)
100DaysOfCode - A GitHub Repo for my #100DaysOfCode challenge projects
awesome-python-data-science - Probably the best curated list of data science software in Python.
SuperStyl - Supervised Stylometry
Py_Trans - Customize Python Syntax
carbon - :black_heart: Create and share beautiful images of your source code