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I found the BEST video explaining how LLMs work 👇

🔗 check out the full video here : https://lnkd.in/dvjZS89d

#LLM #ML #AI #Python

By: https://t.me/DataAnalyticsX

🔖 50 interview questions for LLM

A good warm-up before the interview: 50 questions on Large Language Models in one document. Not in-depth, but as a checklist to test your knowledge — just perfect.

tags: #LLM #ML #python #pytorch

➡ https://t.me/DataAnalyticsX

Data is everywhere. Clarity is rare.⁣
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Behind every dashboard, SQL query, or machine learning model lies a common challenge — understanding the language of data.⁣
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The 𝐀–𝐙 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 brings together 500+ essential terms across SQL, Python, Power BI, Excel, Statistics, and Machine Learning in one structured reference. ⁣
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This is the layer many professionals underestimate.⁣
Not tools. Not dashboards.⁣
But the ability to understand, interpret, and communicate concepts with precision.⁣
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𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞:⁣
- Clear definitions without unnecessary complexity⁣
- Concepts connected across tools and domains⁣
- Coverage from foundational terms to advanced analytics concepts⁣
- Useful for both technical execution and business communication⁣
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𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥:⁣
- During interviews, when explaining concepts matters more than just knowing them⁣
- In projects, where misinterpreting a term can lead to incorrect insights⁣
- In stakeholder discussions, where clarity builds credibility⁣
- In learning journeys, where structured understanding accelerates growth⁣
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𝐒𝐭𝐫𝐨𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐢𝐭𝐬 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞.⁣
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#DataAnalytics #BusinessIntelligence #DataScience #SQL #Python #PowerBI #Excel #MachineLearning #Statistics #DataEngineering #AnalyticsCareer #DataLearning #DataProfessionals #CareerGrowth #InterviewPreparation

https://t.me/DataAnalyticsX

⚡️ 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

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