ML-For-Beginners
ai-seed
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| ML-For-Beginners | ai-seed | |
|---|---|---|
| 28 | 5 | |
| 66,908 | 113 | |
| 3.5% | 1.8% | |
| 7.6 | 1.8 | |
| 18 days ago | about 1 year ago | |
| HTML | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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-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?
ai-seed
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Show HN: AutoAI
Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found:
PyCaret: Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration.
Tpot: Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works.
H2O.ai: They have an open-source flavor, but the best way to use it is the enterprise version on the H2O cloud. The interface is confusing, and the final output is black-box.
Now there are many in the enterprise category, such as DataRobot, AWS SageMaker, Azure etc. Most are unaffordable to Data Scientists unless your employer is sponsoring the platform.
AutoAI: This is 100% automated. Uses GOFAI, Neural Networks and DNN, all in one box. It is 100% White-box. It is the only AutoML framework that generates high-quality (1000s of lines) of Jupyter Notebook code. You can check some example codes here: https://cloud.blobcity.com
- [P] Comparison for all Sklearn Classifiers
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Ready AI Code Templates
Hi, this is the team at BlobCity. Creators of A.I. Cloud (https://cloud.blobcity.com). We just released 400+ ready to use AI seed projects. Code templates provide newbie data scientists a great starting reference. We ourselves find them super useful. Let us know what you all think!
- Show HN: Ready code templates for your next AI Experiment
What are some alternatives?
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
adanet - Fast and flexible AutoML with learning guarantees.
pycaret - An open-source, low-code machine learning library in Python
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
Data-Science-For-Beginners - 10 Weeks, 20 Lessons, Data Science for All!
automlbenchmark - OpenML AutoML Benchmarking Framework
pyVHR - Python framework for Virtual Heart Rate
HungaBunga - HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!
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]
autoai - Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.