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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 67 809 подписчиков, занимая 2 416 место в категории Образование и 5 038 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 67 809 подписчиков.

Согласно последним данным от 09 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 70, а за последние 24 часа — 10, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.94%. В первые 24 часа после публикации контент обычно набирает 2.44% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 1 997 просмотров. В течение первых суток публикация набирает 1 652 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 7.
  • 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 10 июня, 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.

67 809
Подписчики
+1024 часа
+127 дней
+7030 день
Архив постов
🔥 Trending Repository: awesome-deeplearning-resources 📝 Description: Deep Learning and deep reinforcement learning research papers and some codes 🔗 Repository URL: https://github.com/endymecy/awesome-deeplearning-resources 📖 Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme 📊 Statistics: 🌟 Stars: 2.9K stars 👀 Watchers: 221 🍴 Forks: 666 forks 💻 Programming Languages: Not available 🏷️ Related Topics:
#nlp #video #reinforcement_learning #deep_learning #neural_network #code #paper #corpus #modelzoo
================================== 🧠 By: https://t.me/DataScienceN

🔥 Trending Repository: awesome-transformer-nlp 📝 Description: A curated list of NLP resources focused on Transformer networ
🔥 Trending Repository: awesome-transformer-nlp 📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning. 🔗 Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp 📖 Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme 📊 Statistics: 🌟 Stars: 1.1K stars 👀 Watchers: 41 🍴 Forks: 131 forks 💻 Programming Languages: Not available 🏷️ Related Topics:
#nlp #natural_language_processing #awesome #transformer #neural_networks #awesome_list #llama #transfer_learning #language_model #attention_mechanism #bert #gpt_2 #xlnet #pre_trained_language_models #gpt_3 #gpt_4 #chatgpt
================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: SemanticSegmentation_DL 📝 Description: Resources of semantic segmantation based on Deep Learning model 🔗 Repository URL: https://github.com/tangzhenyu/SemanticSegmentation_DL 📖 Readme: https://github.com/tangzhenyu/SemanticSegmentation_DL#readme 📊 Statistics: 🌟 Stars: 1.1K stars 👀 Watchers: 77 🍴 Forks: 315 forks 💻 Programming Languages: Jupyter Notebook - Python - Shell - sed 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

Good news: We have launched the GitHub Top 13 Repo service daily in our channel https://t.me/DataScienceM

There are now free courses available for a limited time on our channel https://t.me/DataScienceC

Python library that adds Generative AI capabilities to Pandas! PandasAI analyzes complex data frames and plot visualizations
Python library that adds Generative AI capabilities to Pandas! PandasAI analyzes complex data frames and plot visualizations just by using natural language. GitHub: https://github.com/sinaptik-ai/pandas-ai

We offer you daily Udemy courses for free and without any fees. https://t.me/DataScienceC

We offer you daily Udemy courses for free and without any fees. https://t.me/DataScienceC

We offer you daily Udemy courses for free and without any fees. https://t.me/DataScienceC

We offer you daily Udemy courses for free and without any fees. https://t.me/DataScienceC

We offer you daily Udemy courses for free and without any fees. https://t.me/DataScienceC

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Data scientists, this is for you — I dug up LeetCode for DS DataLemur — a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here 🍯 👉 https://t.me/DataScienceN 👍

python-docx: Create and Modify Word Documents #python python-docx is a Python library for reading, creating, and updating Mic
python-docx: Create and Modify Word Documents #python python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files. Installation
pip install python-docx
Example
from docx import Document

document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")

document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
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