Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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Сітка:📈 Аналітичний огляд Telegram-каналу Machine Learning
Канал Machine Learning (@machinelearning9) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 40 373 підписників, посідаючи 3 327 місце в категорії Технології та додатки та 225 місце у регіоні Сирія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 40 373 підписників.
За останніми даними від 12 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 399, а за останні 24 години на 24, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 2.42%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.74% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 979 переглядів. Протягом першої доби публікація в середньому набирає 703 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 4.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як distance, insidead, gpu, learning, degree.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Завдяки високій частоті оновлень (останні дані отримано 13 липня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
40 373
Підписники
+2424 години
+1257 днів
+39930 день
Архів дописів
40 391
🔥 Trending Repository: expert
📝 Description: Official Elixir Language Server Protocol implementation
🔗 Repository URL: https://github.com/elixir-lang/expert
🌐 Website: https://www.expert-lsp.org
📖 Readme: https://github.com/elixir-lang/expert#readme
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🌟 Stars: 977 stars
👀 Watchers: 16
🍴 Forks: 20 forks
💻 Programming Languages: Elixir - Erlang
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#elixir #language_server_protocol #lsp================================== 🧠 By: https://t.me/DataScienceM
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🔗 Repository URL: https://github.com/activepieces/activepieces
🌐 Website: https://www.activepieces.com
📖 Readme: https://github.com/activepieces/activepieces#readme
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40 391
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📝 Description: Open Source Inventory Management System
🔗 Repository URL: https://github.com/inventree/InvenTree
🌐 Website: https://docs.inventree.org
📖 Readme: https://github.com/inventree/InvenTree#readme
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🍴 Forks: 1K forks
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🌐 Website: https://mlabonne.github.io/blog/
📖 Readme: https://github.com/mlabonne/llm-course#readme
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40 391
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📖 Readme: https://github.com/juspay/hyperswitch#readme
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🍴 Forks: 3.9K forks
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40 391
🔥 Trending Repository: transformerlab-app
📝 Description: Open Source Application for Advanced LLM + Diffusion Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
🔗 Repository URL: https://github.com/transformerlab/transformerlab-app
🌐 Website: https://transformerlab.ai/
📖 Readme: https://github.com/transformerlab/transformerlab-app#readme
📊 Statistics:
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🔗 Repository URL: https://github.com/mercurjs/mercur
🌐 Website: https://www.mercurjs.com
📖 Readme: https://github.com/mercurjs/mercur#readme
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🔗 Repository URL: https://github.com/nats-io/nats-server
🌐 Website: https://nats.io
📖 Readme: https://github.com/nats-io/nats-server#readme
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💻 Programming Languages: Go
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40 391
🔥 Trending Repository: humanlayer
📝 Description: HumanLayer enables AI agents to communicate with humans in tool-based and async workflows. Guarantee human oversight of high-stakes function calls with approval workflows across slack, email and more. Bring your LLM and Framework of choice and start giving your AI agents safe access to the world. Agentic Workflows, human in the loop, tool calling
🔗 Repository URL: https://github.com/humanlayer/humanlayer
🌐 Website: https://humanlayer.dev
📖 Readme: https://github.com/humanlayer/humanlayer#readme
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📝 Description: All Algorithms implemented in Python
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🔗 Repository URL: https://github.com/microsoft/mcp
📖 Readme: https://github.com/microsoft/mcp#readme
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🌟 Stars: 581 stars
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💻 Programming Languages: C# - PowerShell - Bicep - JavaScript - TypeScript - CSS
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==================================
🧠 By: https://t.me/DataScienceM
40 391
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40 391
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📝 Description: Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
🔗 Repository URL: https://github.com/hashicorp/terraform
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🔥 Trending Repository: cobra
📝 Description: A Commander for modern Go CLI interactions
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🌐 Website: https://cobra.dev
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💻 Programming Languages: Go - Makefile
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40 391
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📝 Description: The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
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