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Machine Learning with Python

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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📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 550 subscribers, ranking 2 402 in the Education category and 4 696 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 550 subscribers.

According to the latest data from 27 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 119 over the last 30 days and by -9 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.75%. Within the first 24 hours after publication, content typically collects 1.56% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 572 views. Within the first day, a publication typically gains 1 068 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
  • Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 28 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

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68 550
Subscribers
-924 hours
-597 days
+11930 days
Posts Archive
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A new collection of free courses has been added: 🔗 https://github.com/dair-ai/ML-Course-Notes Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚 Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠 What's inside: • Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others • A table with lectures, descriptions, videos, notes, and authors • Links to the original lectures and accompanying notes • WIP markers for incomplete materials • Instructions for contributors on adding and improving notes The idea was appreciated. 👍 Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️ #MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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The only LLM cheat sheet you'll ever need 🚀 Covers the main concepts, architectures, and practical applications. ### Basics - Tokens (tokenization, BPE) - Embeddings (cosine similarity) - Attention mechanism (Attention formula, Multi-Head Attention) ### Transformer architecture and its variants - BERT (models with only an encoder) - GPT (models with only a decoder) - T5 (models with an encoder and a decoder) ### Large language models (LLMs) - Prompting (context length, Chain-of-Thought) - Pre-training (SFT, PEFT/LoRA) - Preference tuning (Reward Model, Reinforcement Learning) - Optimizations (Mixture of Experts, Distillation, Quantization) ### Applications - LLM-as-a-Judge (LaaJ) - RAG (Retrieval-Augmented Generation) - Agents (ReAct) - Reasoning models (Scaling) ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A #LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech

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