ChatGPT vs Claude vs Gemini

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Polars Cheat Sheet — The Ultimate Fast DataFrame Guide ⚡🐻‍❄️

Master the most important Polars methods used in real-world data analytics and data science workflows. 📊🚀

This visual cheat sheet covers DataFrame creation, filtering, aggregations, joins, lazy execution, reshaping, sorting, and more — with practical examples and simulated outputs for faster learning. 🧠💻

Perfect for:
• Data Analysts 👩‍💼
• Data Scientists 🧪
• Python Developers 🐍
• Big Data Enthusiasts 🌐

🚀 Built for speed with Rust-powered performance. ⚙️
📌 Save this post for your next data project.

Source: DataAnalyticsX

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸

Join our channel today for free! Tomorrow it will cost 500$!

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You can join at this link! 👆👇

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🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸

Join our channel today for free! Tomorrow it will cost 500$!

https://t.me/+-WZeIeP8YI8wM2E6

You can join at this link! 👆👇

https://t.me/+-WZeIeP8YI8wM2E6

🔖 Collecting free tokens from all LLM providers in one project 🤖✨

The developer has created an open-source tool: you add API keys from platforms with free limits. 🔑💻

The system automatically switches between them when one runs out. 🔄🚀

⛓️ Link to GitHub
https://github.com/tashfeenahmed/freellmapi

#LLM #FreeTokens #OpenSource #AI #Developer #Tech

⚡️ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" 🤖

A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. 🚀

Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. 🧠

The roadmap is divided into 7 tracks: 📊

1. Foundation: Python, mathematics, statistics, tools 🏗️
2. Classic ML: scikit-learn, tabular data, metrics, validation 📈
3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖
5. Generative AI: images, videos, audio, multimodality 🎨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️
7. Specialization: CV, NLP, RecSys, RL, Safety 🎯

The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫

In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. 🛠️

A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. 🛑

In terms of time, it's no fairy tale either: ⏳

1. 0-3 months: mathematics, classic ML 📚
2. 3-6 months: Deep Learning and PyTorch 🔥
3. 6-12 months: LLM, RAG, fine-tuning, AI agents 🤖
4. 12+ months: MLOps, production, scaling, specialization 🚀

Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! 🎓

If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. 🗺️

https://github.com/justxor/MachineLearningRoadmap 🔗

#MachineLearning #AI #DataScience #LLM #MLOps #Python

🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨

We found this interactive website that shows you visually how transformer models work. 🌐📊

Transformer Explainer:
https://poloclub.github.io/transformer-explainer/

#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech

Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊

pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.

Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗.

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

#DataScience #Pandas #Polars #DuckDB #Python #Analytics