现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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
显示更多📈 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 2 416，并在 印度 地区排名第 5 038 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 67 813 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 70，过去 24 小时变化为 10，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 2.94%。内容发布后 24 小时内通常能获得 2.44% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 997 次浏览，首日通常累积 1 652 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 7。
- 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 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 教育 类别中的关键影响点。
67 813
订阅者
+1024 小时
+127 天
+7030 天
帖子存档
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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✅ https://t.me/Codeprogrammer
Gradio: The easiest way to demo your models.
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If you're looking to create interactive demos for your ML project, check out #Gradio!
♻️ Repost if you found this useful
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A Complete Course to Learn Robotics and Perception
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📂 8 Steps to Mastering MLOps
✅ For data scientists
⏯️ Introduction to MLOps
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2️⃣ Model Management
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3️⃣ Building a pipeline of models
📎 Building End-to-End ML Pipelines
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5️⃣ Introduction to Docker
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Open Guide to Data Structures and Algorithms
A must-read for anyone starting their journey in computer science and programming. This open-access book offers a clear, beginner-friendly introduction to the core concepts of data structures and algorithms, with simple explanations and practical examples. Whether you're a student or a self-learner, this guide is a solid foundation to build your DSA knowledge. Highly recommended for those who want to learn efficiently and effectively.
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Datasets Guide 📚
A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.
Importance:
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Link: https://docs.unsloth.ai/basics/datasets-guide
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Master PyTorch Faster with These Free Resources!
Whether you're just getting started with PyTorch or looking to refresh your deep learning skills, these two resources are all you need:
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A concise reference guide packed with essential PyTorch commands and patterns. Perfect for quick look-ups during development.
Download:
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A beginner-friendly PDF with practical examples to help you build and train deep learning models using PyTorch from scratch.
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Foundations of Large Language Models
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