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Data science/ML/AI

Data science/ML/AI

前往频道在 Telegram

Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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📈 Telegram 频道 Data science/ML/AI 的分析概览

频道 Data science/ML/AI (@datascience_bds) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 13 674 名订阅者,在 技术与应用 类别中位列第 9 377,并在 印度 地区排名第 31 635

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 13 674 名订阅者。

根据 09 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 155,过去 24 小时变化为 5,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 8.03%。内容发布后 24 小时内通常能获得 2.25% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 098 次浏览,首日通常累积 308 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 panda, learning, row, api, ethic 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:
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...

凭借高频更新(最新数据采集于 10 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 技术与应用 类别中的关键影响点。

13 674
订阅者
+524 小时
+197
+15530
帖子存档
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Data Science vs Mathematics

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Accelerate Data Science Workflows with Zero Code Changes by nvidia Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows. By participating in this course, you will: Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes Experience the significant reduction in processing time when workflows are GPU-accelerated Prerequisites: Basic understanding of data processing and knowledge of a standard data science workflow on tabular data Experience using common Python libraries for data analytics Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX 🆓 Free Online Course ⏰ Duration : More than 1 hour 🏃‍♂️ Self paced ✅ Certification available Course Link #datascience #nvidia  ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

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Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻 It's easy with http://Telega.io. As the leading platform for native ads and integrations on Telegram, it provides user-friendly and efficient tools for quick and automated ad launches. ⚡️ Place your ad here in three simple steps: 1 Sign up: https://telega.io/c/datascience_bds 2 Top up the balance in a convenient way 3 Create your advertising post If your ad aligns with our content, we’ll gladly publish it. Start your promotion journey now!

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Flow chart of commonly used statistical tests
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Introduction to Probability and Statistics for Engineers List of probability and statistics cheatsheets by Stanford
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Brain of an AI Engineer
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The LLM Scientist Roadmap
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LLMOps vs MLOps
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Design patterns for AI Agentic workflow in LLM applications
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