eurybia
ML-For-Beginners
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| eurybia | ML-For-Beginners | |
|---|---|---|
| 3 | 28 | |
| 203 | 66,806 | |
| 3.0% | 3.3% | |
| 5.2 | 8.0 | |
| 28 days ago | 8 days ago | |
| Jupyter Notebook | HTML | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
eurybia
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State of the Art data drift libraries on Python?
Try out eurybia, from the author of shapash which is a brilliant library as well.
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Providing ML team with data: normalized or denormalized?
Your data scientists will cook up ugly bits of code to prepare their training data, you'll probably have to rewrite that when they want to ship to prod and also detect and handle discrepancies. In that regard, it sounds like you may enjoy Eurybia to communicate about this data with your data scientists. We made it precisely for that.
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Advice on a Data Quality framework
So we just trained a model to try and do the same, and then sort of read its entrails through Shapash. The more it can tell the difference, the more your data has changed. We can know which variable has changed the most, and how much it's important to our models. If all else fails (and also if all else works), we can still know (again, this is all quantified in some way, we need numbers, not eyeballings) how much our models predictions have evolved over time, independantly of particular data changes, legit or not. How can our models predictions change if the data is all clean, you ask ? I mean I asked, but you would have too, in my shoes. What lies beyond data engineering ? What is the meaning of life ? The answer is concept drift, and that's where we're starting to work on now that we have a good grasp on data drift. Anyways, the tool is Eurybia. If any part of my ramblings resemble some of your work, please give it a try and chat us up here or through the repo, we are of course very eager to get feedbacks and possibly even contributions, who knows. See ya !
ML-For-Beginners
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Good coding groups for black women?
- https://github.com/microsoft/ML-For-Beginners
Also check out this list Pitt puts out every year:
- FLaNK Stack Weekly for 20 Nov 2023
- ML for Beginners GitHub
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is it worth learning NLP without master degree?
I don't recommend just jumping in into natural language processing directly without understanding artificial intelligence theory. I personally recommend for you to start with the basic stuff (regression, classification, and clustering, for example), and then jump into more advanced topics. You already know software developer stuff, so that's a big step already, and it should be easier to understand some concepts. Maybe follow Microsoft's machine learning for beginners curriculum? It looks like a good roadmap overall to not instantly burn out on nlp
- AI i Machine Learning
- I want to learn more about AI and Machine Learning
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Pocetak ML karijere
https://github.com/microsoft/ML-For-Beginners jel mislis na ovo?
- How could I have known
- GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- How do I reset my career after already getting my masters?
What are some alternatives?
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
nannyml - nannyml: post-deployment data science in python
pycaret - An open-source, low-code machine learning library in Python
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Data-Science-For-Beginners - 10 Weeks, 20 Lessons, Data Science for All!
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
pyVHR - Python framework for Virtual Heart Rate
S2ML-Art-Generator - Multiple notebooks which allow the use of various machine learning methods to generate or modify multimedia content [Moved to: https://github.com/justin-bennington/S2ML-Generators]
amazon-denseclus - Clustering for mixed-type data