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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网络:Machine Learning with Python 印度5 049 教育2 404...

📈 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 67 812 名订阅者，在教育类别中位列第 2 404，并在印度地区排名第 5 049 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 67 812 名订阅者。

根据 05 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 77，过去 24 小时变化为 9，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 2.60%。内容发布后 24 小时内通常能获得 2.50% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 767 次浏览，首日通常累积 1 695 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 07 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。

67 812

订阅者

+924 小时

+587 天

+7730 天

1 767

帖子浏览量

~ 1 69524 小时

~ 2 25748 小时

2.60%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

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YOLO Training Template Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created. The platform allows: • to upload your own dataset • to label manually or auto-label via DINOv3 • to enrich the data if desired • to train a #YOLO model on your own data • to run inference immediately • to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones All of this is available for free and can already be tested on #GitHub. Repo: https://github.com/computer-vision-with-marco/yolo-training-template https://t.me/CodeProgrammer

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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 ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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Machine Learning Roadmap 2026 #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python https://t.me/CodeProgrammer

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https://www.threads.com/@datasciencem

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أنا أستخدم ‌‎Threads‌‏ باسم @‌‎datasciencem‌‏. يرجى تثبيت التطبيق لمتابعة المقتطفات والردود الخاصة بي. ‌‎https://www.threads.com/@datasciencem?invite=0‌‏

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Deep Delta Learning Read Free: https://www.k-a.in/DDL.html #DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience https://t.me/CodeProgrammer

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1+1=?

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These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯 Repo: https://udlbook.github.io/udlbook/ 👉 @codeprogrammer

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Repost from Machine Learning

4 learning paradigms in machine learning, explained visually: 1. Transfer Learning 2. Fine-tuning 3. Multi-task Learning 4. Federated Learning 👉 @DataScienceM

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🧠 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍)⁣ 🔹 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐨𝐝𝐚𝐲⁣ 𝐖𝐡𝐚𝐭 𝐊𝐍𝐍 𝐢𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬⁣ 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐊𝐍𝐍 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧⁣ 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐊 (𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫)⁣ 𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: 𝐄𝐮𝐜𝐥𝐢𝐝𝐞𝐚𝐧 𝐯𝐬 𝐌𝐚𝐧𝐡𝐚𝐭𝐭𝐚𝐧⁣ 𝐖𝐡𝐲 𝐊𝐍𝐍 𝐢𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐚 𝐥𝐚𝐳𝐲 / 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐞𝐫⁣ ⁣ 🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣ ⁣ 1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘒-𝘕𝘦𝘢𝘳𝘦𝘴𝘵 𝘕𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘴 (𝘒𝘕𝘕)?⁣ 2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘒𝘕𝘕 𝘤𝘢𝘭𝘭𝘦𝘥 𝘢 𝘭𝘢𝘻𝘺 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮?⁣ 3️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘒𝘕𝘕 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘒𝘕𝘕 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣ 4️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘒?⁣ 5️⃣ 𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘸𝘩𝘦𝘯 𝘒 𝘪𝘴 𝘵𝘰𝘰 𝘴𝘮𝘢𝘭𝘭 𝘰𝘳 𝘵𝘰𝘰 𝘭𝘢𝘳𝘨𝘦?⁣ 6️⃣ 𝘞𝘩𝘢𝘵 𝘥𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘒𝘕𝘕?⁣ 7️⃣ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮 𝘱𝘰𝘰𝘳𝘭𝘺 𝘰𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢?⁣ 8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘰𝘧 𝘒𝘕𝘕?⁣ 9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘒𝘋-𝘛𝘳𝘦𝘦 𝘢𝘯𝘥 𝘉𝘢𝘭𝘭-𝘛𝘳𝘦𝘦 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦?⁣ 🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 #𝘒𝘕𝘕?⁣ https://t.me/CodeProgrammer ⭐️

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Machine Learning Interview prep repo: https://github.com/khangich/machine-learning-interview?tab=readme-ov-file

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