ML-Course-Notes VS Failed-ML

Compare ML-Course-Notes vs Failed-ML and see what are their differences.

ML-Course-Notes

🎓 Sharing machine learning course / lecture notes. (by dair-ai)
Machine Learning Deep Learning AI Natural Language Processing Data Science
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Failed-ML

Compilation of high-profile real-world examples of failed machine learning projects (by kennethleungty)
AI Artificial intelligence Data Science Deep Learning Machine Learning ML failed-ml Computer Vision data-engineering data-quality Natural Language Processing Production recsys failed-machine-learning failed-data-science Regression Forecasting Classification fml
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towardsdatascience.com
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ML-Course-Notes Failed-ML
117 1
5,819 617
0.0% -
0.0 5.2
about 1 year ago 2 months ago
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

ML-Course-Notes

Posts with mentions or reviews of ML-Course-Notes. We have used some of these posts to build our list of alternatives and similar projects.

Failed-ML

Posts with mentions or reviews of Failed-ML. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing ML-Course-Notes and Failed-ML you can also consider the following projects:

ML-YouTube-Courses - 📺 Discover the latest machine learning / AI courses on YouTube.

RubixML - A high-level machine learning and deep learning library for the PHP language.

BetaML.jl - Beta Machine Learning Toolkit

Transformers-Recipe - 🧠 A study guide to learn about Transformers

squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:

yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

AI-Conference-Info - Extensive acceptance rates and information of main AI conferences

applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

pycm - Multi-class confusion matrix library in Python

classifai - Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence.

ML-Course-Notes vs ML-YouTube-Courses Failed-ML vs ML-YouTube-Courses ML-Course-Notes vs RubixML Failed-ML vs BetaML.jl ML-Course-Notes vs Transformers-Recipe Failed-ML vs squirrel-core Failed-ML vs yt-channels-DS-AI-ML-CS Failed-ML vs AI-Conference-Info Failed-ML vs RubixML Failed-ML vs applied-ml Failed-ML vs pycm Failed-ML vs classifai