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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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πŸ“ˆ 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 68 109 subscribers, ranking 2 375 in the Education category and 4 809 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 109 subscribers.

According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 120 over the last 30 days and by -14 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.55%. Within the first 24 hours after publication, content typically collects 2.02% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 3 099 views. Within the first day, a publication typically gains 1 378 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œLearn 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 27 August, 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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68 109
Subscribers
-1424 hours
-667 days
+12030 days
Posts Archive
Reinforcement Learning Methods and Tutorials In these tutorials for reinforcement learning, it covers from the basic RL algor
Reinforcement Learning Methods and Tutorials In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms.

πŸš€ Looking for a portfolio-ready NLP project? I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset. You’ll see how far classical NLP can go with: πŸ“ Bag-of-Words and TF-IDF πŸ”€ Character n-grams πŸ“Š Model comparison 🧩 Ensemble stacking It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step: πŸ”— https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

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πŸ”– One of the most useful books on Agentic AI This is not just a textbook, but a comprehensive overview of modern LLMs, model
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πŸ”– One of the most useful books on Agentic AI This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents. It's an excellent option to get a holistic picture and understand which topics deserve deeper study. ⛓️ Link to the book https://arxiv.org/abs/2606.24937

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Understanding Datasets πŸ˜‰
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πŸ”₯ Free IT Cert Resources – Grab Them While They're Hot! 🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity πŸ’₯No signup traps, no hidden fees – just click and download. πŸ“˜ FREE Cert E‑Book β†’ https://bit.ly/4wkiLAT πŸͺœ Online FREE Course β†’ https://bit.ly/4vHFJSz ☁️ FREE AI Materials β†’ https://bit.ly/4wdu7X6 πŸ“Š Cloud Study Guide β†’ https://bit.ly/4y0HyeW 🧠 Free Mock Exam β†’ https://bit.ly/4ff8jos Tag a friend who's also on this journey – Get certified together! πŸ’ͺ 🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/ πŸ“² Need personalized help? β†’ https://wa.link/6k7042

Transformers become more understandable when you can "poke" the model directly. 🧠✨ Transformer Explainer is an interactive v
Transformers become more understandable when you can "poke" the model directly. 🧠✨ Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. πŸ” It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. πŸ”„πŸ“ Key features: 🌟 - Live GPT-2 in the browser - experiment without setting up a separate model server πŸ’» - Your own text - try your own prompts and see how the model processes them ✍️ - Internal components - observe the operations working inside the transformer πŸ”§ - Focus on predicting the next token - link each visual step to the model's predictions 🎯 - Local development - clone the repository, install dependencies, and run via npm for in-depth study βš™οΈ It's open-source (MIT license). πŸ“œ https://github.com/poloclub/transformer-explainer #AI #MachineLearning #GPT #DataScience #TechTools #OpenSource ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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