Python Courses & Resources
前往频道在 Telegram
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
显示更多📈 Telegram 频道 Python Courses & Resources 的分析概览
频道 Python Courses & Resources (@python53) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 49 139 名订阅者,在 教育 类别中位列第 3 607,并在 印度 地区排名第 7 813 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 49 139 名订阅者。
根据 10 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 542,过去 24 小时变化为 35,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 2.51%。内容发布后 24 小时内通常能获得 1.13% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 1 235 次浏览,首日通常累积 554 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 3。
- 主题关注点: 内容集中在 consultation, dentist, insidead, waybienad, appointment 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:
“Master Python with step-by-step courses – from basics to advanced projects and practical applications.”
凭借高频更新(最新数据采集于 11 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。
49 139
订阅者
+3524 小时
+1277 天
+54230 天
帖子存档
Repost from Machine Learning with Python
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In this rapidly changing age of AI technology adoption, it is even more critical to understand how to secure IT systems. This course is designed to teach you fundamental cyber security concepts to kick-start your security learning. It is vendor agnostic and is divided into small lessons that should take around 30-60 mins to complete. Each lesson has a small quiz and links to further reading if you want to dive into the topic a bit more
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Repost from Data Science Books
Python for Natural Language Processing (2024 - 3th)
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Football Analysis in Python with Yolov8 & OpenCV
Video: https://youtu.be/yJWAtr3kvPU?si=Ix9t3QiXR3jwbog7
Deep learning web application for football analysis with Streamlit.
Used material links:
Project repo: https://github.com/Hmzbo/Football-Analytics-with-Deep-Learning-and-Computer-Vision
Custom dataset tips: https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/
Annotation tools:
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cvat: https://github.com/opencv/cvat
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A course for absolute beginners that want to learn Python and pass the PCEP exam (Certified Entry-Level Python Programmer)
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Repost from Machine Learning with Python
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