NumPy looks like a calculator and behaves like a memory model. 🧮

Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses.

15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. 🚀

Arrays, shapes and the machinery under the math:
https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true

https://t.me/CodeProgrammer ⭐️

🔖 Interactive tool for learning machine learning

A Python library has been released on GitHub that allows you to create your own datasets and use them for machine learning experiments.

Features:
🔵 Create 2D datasets directly in Jupyter;
🔵 Draw points and assign classes using the mouse;
🔵 Export data to pandas and Polars;
🔵 Pass samples to scikit-learn;
🔵 Experiment with classification and regression;
🔵 Update model training when data changes;
🔵 Works in Jupyter, VS Code, and Google Colab.

There is a demo version that you can try without installation.

⛓Link to GitHub
https://github.com/koaning/drawdata

https://t.me/CodeProgrammer 😍