MindsDB VS scikit-learn

If you would like to find out more about MindsDB, you can visit their website.Feel free to bookmark MindsDB's Github repository and give them a star.Engage with the MindsDB community on Slack or GitHub to ask questions and share your ideas and thoughts.

Not a programming language, but a database solution, called MindsDB. From the tab "How does MindsDB work?":

I picked a few of them on the hacktoberswag.com website. Precisely three: pusher.js, refine.dev, and MindsDB. You are probably asking Oh, you did not pick Wasp? The thing was, Wasp wasn’t listed on that web page and I didn’t know if any tool by that name existed.

MindsDB GitHub

The very first thing to do is to create your 30-day free to use Mindsdb account. Once this is done you are all set to explore and understand the MindsDB way of building machine learning models.

Check out https://github.com/mindsdb/mindsdb/ it's an ML project but has plenty of non-ML areas to contribute to. As well as a few no-code things too.

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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 documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.

1. Scikit-learn Scikit-learn is a must-know Python library for any data scientist. It offers a wide range of machine learning algorithms, data preprocessing tools, and model evaluation metrics that are easy to use and highly efficient. Whether you’re working on regression, classification, or clustering tasks, Scikit-learn has got you covered.

There's an issue here about that https://github.com/scikit-learn/scikit-learn/discussions/25450

This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning theory: types of machine learning problems like regression and classification, the concept and process of Supervised machine learning (fit/predict and evaluate quality using metrics) and common models used for it, including Random Forest Classifier, and it's implementation in SciKit-Learn Python library. Additionally, it would be great if you previously participated in Kaggle competitions, because to understand and run all code of this article you need to have an account on https://kaggle.com.

Scikit-learn : A Python module for machine learning build on top of SciPy

Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e219...