现已上线！2025 年 Telegram 研究 — 年度关键洞察 
AI and Machine Learning
前往频道在 Telegram
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
显示更多📈 Telegram 频道 AI and Machine Learning 的分析概览
频道 AI and Machine Learning (@machine_learning_courses) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 94 144 名订阅者，在 教育 类别中位列第 1 548，并在 印度 地区排名第 2 998 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 94 144 名订阅者。
根据 28 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 939，过去 24 小时变化为 37，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 7.03%。内容发布后 24 小时内通常能获得 2.49% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 6 622 次浏览，首日通常累积 2 343 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 9。
- 主题关注点： 内容集中在 learning, llm, linkedin, linux, udemy 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, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
凭借高频更新（最新数据采集于 29 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。
94 144
订阅者
+3724 小时
+2867 天
+93930 天
帖子存档
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🔰 Artificial Intelligence A-Z™ 2023: Build an AI with ChatGPT4
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🔸 Full description 🔸
Every year, it seems, there is a new hot trend in data science. One of the hottest predictive analytics algorithms this year is gradient-boosted trees. One cannot hope to understand why it is popular and successful if one doesnt understand the basics of decision trees. Specific tree algorithms have risen and fallen in popularity, but the core concepts have been fundamental to the discipline for at least 30 years. In this course, instructor Keith McCormick demonstrates and discusses a half-dozen popular decision tree algorithms. Keith shows how to access them using other open-source options from within the KNIME platform. He explains them and reverse engineers them to create a solid foundation on which to build more advanced data science skills.
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🔅 Machine Learning and AI Foundations: Advanced Decision Trees with KNIME
🔊 Author: Keith McCormick
🔸 Date: 2022-11-14
⏰ Duration: 1h 33m
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Learn to go beyond the basic decision tree algorithms in KNIME by accessing WEKA, R, and Python-based decision tree and rule induction algorithms from within the KNIME platform.
📗 Topics: Decision Trees, Knime, Machine Learning
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