Machine Learning
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Real Machine Learning — simple, practical, and built on experience.
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Foundations of Applied Mathematics is a free series of four textbooks created for the applied and computational mathematics program at Brigham Young University. 📚

The series includes four volumes:
*   Mathematical Analysis
*   Algorithms, Approximation, and Optimization
*   Uncertainty and Data
*   Dynamics and Control

The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. 🐍

I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. 💻

https://foundations-of-applied-mathematics.github.io

#Mathematics #Python #Education #DataScience #Algorithms #Learning

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A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

#Pandas #Python #DataScience #Performance #Fireducks #BigData

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🔖 Learning Data Science through interactive examples

One of the most useful repositories for those who want to better understand machine learning.

It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.

Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython

#DataScience #MachineLearning #Python #Learning #Tech #GitHub

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