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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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📈 Аналитический обзор Telegram-канала Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 109 подписчиков, занимая 2 375 место в категории Образование и 4 809 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 109 подписчиков.

Согласно последним данным от 26 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 120, а за последние 24 часа — -14, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.55%. В первые 24 часа после публикации контент обычно набирает 2.02% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 3 099 просмотров. В течение первых суток публикация набирает 1 378 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Благодаря высокой частоте обновлений (последние данные получены 27 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

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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.

🚀 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: 📝 Bag-of-Words and TF-IDF 🔤 Character n-grams 📊 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: 🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

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🔖 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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🔖 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 😉
Understanding Datasets 😉

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Hands-On Python Machine Learning with Real World Projects Python Based Machine Learning Course with Practical Exercises and C
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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. 🔍 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. 🔄📝 Key features: 🌟 - Live GPT-2 in the browser - experiment without setting up a separate model server 💻 - Your own text - try your own prompts and see how the model processes them ✍️ - Internal components - observe the operations working inside the transformer 🔧 - 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 ⚙️ It's open-source (MIT license). 📜 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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