Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Forwarded from Machine Learning
πŸš€ Master Binary Classification with Neural Networks! 🧠✨

Ever wondered how to build a neural network from scratch in Python using NumPy? πŸπŸ“Š

Binary classification is at the heart of many machine learning applications. πŸŽ―πŸ€–

Our super-detailed guide walks you through the entire process step by step. πŸ“πŸ“š

πŸ’‘ Dive in and start building your own neural network today! πŸ—πŸ”₯
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/

#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
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Forwarded from Data Analytics
Pandas vs Polars vs DuckDB: Which Library Should You Choose? πŸ€”πŸ“Š

pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows πŸ“πŸ“ˆ. Polars focus on fast, memory-efficient DataFrame processing βš‘πŸ’Ύ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics πŸ—„οΈπŸ”.

Each tool fits a different kind of local data workflow πŸ› οΈ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases πŸ†πŸ”—.

More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ πŸ”—

#DataScience #Pandas #Polars #DuckDB #Python #Analytics
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Forwarded from Machine Learning
πŸ”– A huge open-source course on AI Engineering from scratch

In the repository, we've collected:
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Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. πŸš€

⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch

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Stop discovering ML Python libraries one random tutorial at a time πŸ›‘

Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem πŸ“š.

It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers πŸ“Š.

Key features:

β€’ 920-project index – a large scan-friendly map of open-source ML Python projects πŸ—ΊοΈ
β€’ 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more 🧩
β€’ Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals βš™οΈ
β€’ Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies πŸ“ˆ
β€’ Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits πŸ”„

It’s open-source (CC BY-SA 4.0 license) πŸ“œ.

https://github.com/lukasmasuch/best-of-ml-python πŸ”—

#MachineLearning #Python #ML #OpenSource #DataScience #TechStack

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✍️ Pyneng β€” a large base for Python and network automation!

Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.

πŸ“Œ I'll leave a link: https://pyneng.readthedocs.io/en/latest/

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