Machine Learning with Python

前往频道在 Telegram

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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网络:Machine Learning with Python 印度5 035 教育2 428...

📈 Telegram 频道 Machine Learning with Python 的分析概览

频道 Machine Learning with Python (@codeprogrammer) 英语语言赛道中的是活跃参与者。目前社区聚集了 67 833 名订阅者，在教育类别中位列第 2 428，并在印度地区排名第 5 035 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 67 833 名订阅者。

根据 15 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 82，过去 24 小时变化为 13，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 4.40%。内容发布后 24 小时内通常能获得 1.74% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 983 次浏览，首日通常累积 1 177 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 5。
主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 16 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

67 833

订阅者

+1324 小时

+187 天

+8230 天

2 983

帖子浏览量

~ 1 17724 小时

~ 1 70948 小时

4.40%

参与率

~ 4

每日帖子数

Ads index

beta

帖子存档

67 832

Repost from Data Science Books

We have just launched a fundraising campaign for the channel to ensure continued quality service We upload the book via Internet data, and this is expensive for us Participate and contribute to the donation campaign until the target amount is reached Members who will contribute to the donation campaign will receive a free subscription to the paid channel and a LinkedIn grant Donate link: https://boosty.to/datascienceteam/donate

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Repost from Data Science Books

We have just launched a fundraising campaign for the channel to ensure continued quality service We upload the book via Internet data, and this is expensive for us Participate and contribute to the donation campaign until the target amount is reached Donate link: https://boosty.to/datascienceteam/donate

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غدا سنطلق حملة جمع تبرعات للقناة، وسيتم عرض نسبة التقدم أولا من خلال رسالة مثبتة نأمل منكم المشاركة في التبرعات إذا كنتم قادرين على التبرع Tomorrow we will launch a fundraising campaign for the channel, and the progress rate will be displayed first through a pinned message We hope that you will participate in donations if you are able to donate

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🖥 Get domain name information using Python $pip install whois $pip install whois

>>> import whois
>>> domain = whois.query('google.com')

>>> print(domain.dict)
{
 'expiration_date': datetime.datetime(2020, 9, 14, 0, 0),
 'last_updated': datetime.datetime(2011, 7, 20, 0, 0),
 'registrar': 'MARKMONITOR INC.',
 'name': 'google.com',
 'creation_date': datetime.datetime(1997, 9, 15, 0, 0)
}

>>> print(domain.name)
google.com

>>> print(domain.expiration_date)
2024-09-14 00:00:00

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How to Perform Face Detection with Deep Learning Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets. One example is the Multi-task Cascade Convolutional Neural Network, or MTCNN for short. In this tutorial, you will discover how to perform face detection in Python using classical and deep learning models. https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

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💼 Briefcase Briefcase is a tool for converting a Python project into a standalone native application. ▪ Github: https://github.com/beeware/briefcase ▪Tutorial: https://briefcase.readthedocs.io/en/stable/ https://t.me/CodeProgrammer Please more reaction with our posts

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