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 68 482 подписчиков, занимая 2 406 место в категории Образование и 4 586 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 482 подписчиков.
Согласно последним данным от 04 ноября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -75, а за последние 24 часа — 7, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.38%. В первые 24 часа после публикации контент обычно набирает 1.57% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 314 просмотров. В течение первых суток публикация набирает 1 073 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 9.
- 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 05 ноября, 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.
68 482
Подписчики
+724 часа
-597 дней
-7530 дней
Архив постов
Google just dropped Generative AI learning path with 9 courses:
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Repost from AI & ML Papers
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Repost from AI & ML Papers
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80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife
Classification library
https://github.com/Tessellate-Imaging/monk_v1
Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo
Detection and Segmentation Library
https://github.com/Tessellate-Imaging/
Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo
https://t.me/DataScienceT
Repost from AI & ML Papers
Data Science With Python Workflow Cheat Sheet
Creator: business Science
Stars ⭐️: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf
https://t.me/DataScienceT
How do Transformers work?
All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!
This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task
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https://t.me/DataScienceT
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⭐️ Python Courses:
https://t.me/Python53Speech to Text using Python
✅ More ♥️♥️ = more posts
@CodeProgrammer ♥️
The Data Science and Python channel is for researchers and advanced programmers
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