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Machine Learning with Python

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

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📈 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 67 813 名订阅者,在 教育 类别中位列第 2 427,并在 印度 地区排名第 5 028

📊 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 67 813 名订阅者。

根据 13 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 60,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 4.31%。内容发布后 24 小时内通常能获得 1.69% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 2 926 次浏览,首日通常累积 1 148 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 6
  • 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 14 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。

67 813
订阅者
-324 小时
-77
+6030
帖子存档
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Are you fluent in Python and want to evaluate your skills? 🤔 Do you want to learn Python? 🤗 Are you interested in learning through questions and answers? 😵 Do you want to receive the explanation of the question?💡 🟢 https://t.me/DataScienceQ 👍

Learn PyTorch in 4 steps 👨🏻‍💻 With support for more than 200 different mathematical operations, PyTorch is one of the most powerful open source and computational libraries based on Python for machine learning. You don't need to look for different sources to learn this popular library, but the website of this library covers more than what you need.💯 2️⃣ Here, I will teach you how to learn PyTorch from scratch in 4 steps. 👌🏼 1️⃣ Text tutorial: If you don't know anything about Python, you should start learning from here. ⬅️ Topics: tensors, datasets and DataLoader, model building, optimization loop, saving, loading and using the model. 📄 PyTorch Basics 📎 Link: Learn the Basics 2️⃣ Video training: This section is very useful for those who prefer video content. ⬅️ Topics: introduction, tensor construction and model construction with pytorch. 📄 Introduction to PyTorch 📎 Link: Introduction to PyTorch 3️⃣ Examining examples: By viewing different examples, you will review what you have learned so far. 📄 Learning Python by example 📎 Link: Learning PyTorch with Examples 4️⃣ Coding: This step is vital! Your theoretical knowledge is worthless without coding! The Keras website is full of sample code that is great for getting started. 📄 Start coding 📎 Link: keras.io

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