现已上线！2025 年 Telegram 研究 — 年度关键洞察 
DataSpoof
前往频道在 Telegram
Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data
显示更多📈 Telegram 频道 DataSpoof 的分析概览
频道 DataSpoof (@dataspoof) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 16 218 名订阅者，在 教育 类别中位列第 12 491，并在 印度 地区排名第 27 172 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 16 218 名订阅者。
根据 04 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 -189，过去 24 小时变化为 -3，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 10.03%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 0 次浏览，首日通常累积 0 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 0。
- 主题关注点： 内容集中在 api, llm, pipeline, +9183182, engineer 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 Data Science
https://dataspoof4081.graphy.com/membership
Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data”
凭借高频更新（最新数据采集于 05 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。
16 218
订阅者
-324 小时
-327 天
-18930 天
帖子存档
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Complete post
https://www.linkedin.com/posts/abhishek-kumar-singh-8a6326148_datascience-genai-activity-7445330995358388224-NYkz?utm_source=share&utm_medium=member_android&rcm=ACoAACOks34BFSi927IWily_2kgESJV38GQlIWQ
Follow @dataspoof to learn about #datascience and #genai
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Repost from DataSpoof
https://us06web.zoom.us/meeting/register/QlVhFB5cQh2_I1C2taKX7w
Link to register
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Webinar will start in 20 minutes
https://us06web.zoom.us/meeting/register/QlVhFB5cQh2_I1C2taKX7w
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Follow us on Instagram www.instagram.com/dataspoof and read the complete caption if you want to know about NATGRID
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Today most of the student or working professionals get failed on their system design interviews for their roles on machine learning engineer, data scientist and AI Engineer.
So we here at DataSpoof, Our team prepared Notes on the system design (more than 100+ case studies) from more than 70+ top companies
https://rzp.io/rzp/8Wl4MBZY
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Problems with Today's large language model
Follow us on Instagram www.instagram.com/dataspoof for data science and genai updates
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What is weird generalization In Machine learning
Weird generalization usually refers to a surprising behavior of machine learning models where they perform well on data they were never explicitly trained for, but in a way that doesn’t align with human intuition.
In simple terms
A model learns patterns that work, but not necessarily the patterns humans expect.
