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
显示更多📈 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 67 835 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 67 835 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。
67 835
订阅者
+1324 小时
+187 天
+8230 天
帖子存档
Get started in Data Science with Microsoft's FREE course for beginners.
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Repost from Machine Learning with Python
Best Data Science Channels and groups on Telegram:
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Repost from AI & ML Papers
Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
Please update telegram version
Google just dropped Generative AI learning path with 9 courses:
🤖: Intro to Generative AI
🤖: Large Language Models
🤖: Responsible AI
🤖: Image Generation
🤖: Encoder-Decoder
🤖: Attention Mechanism
🤖: Transformers and BERT Models
🤖: Create Image Captioning Models
🤖: Intro to Gen AI Studio
🌐 Link: https://www.cloudskillsboost.google/paths/118
https://t.me/DataScienceT
Repost from AI & ML Papers
Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
Please update telegram version
Repost from AI & ML Papers
Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
Please update telegram version
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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