Machine Learning
前往频道在 Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Machine Learning 的分析概览
频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 114 名订阅者,在 技术与应用 类别中位列第 3 384,并在 叙利亚 地区排名第 231 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 40 114 名订阅者。
根据 24 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 401,过去 24 小时变化为 38,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 1.96%。内容发布后 24 小时内通常能获得 1.16% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 788 次浏览,首日通常累积 465 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 2。
- 主题关注点: 内容集中在 distance, insidead, gpu, learning, degree 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:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新(最新数据采集于 25 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。
40 114
订阅者
+3824 小时
+637 天
+40130 天
帖子存档
40 118
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Repost from Machine Learning with Python
I often see people say that it's impossible to enter the IT field without expensive courses.
However, there's a huge amount of high-quality materials available for free:
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Multi-Label Text Classification with Scikit-LLM 📝
In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. 📊
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. ⚙️
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ 🔗
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Repost from Machine Learning with Python
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