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Machine Learning

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

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📈 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 41 668 名订阅者,在 技术与应用 类别中位列第 3 145,并在 叙利亚 地区排名第 215 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 41 668 名订阅者。

根据 05 十月, 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 533,过去 24 小时变化为 13,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 6.25%。内容发布后 24 小时内通常能获得 1.92% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 2 601 次浏览,首日通常累积 800 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 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 06 十月, 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。

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41 668
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+1324 小时
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+53330 天
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+1 638
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日期
订阅者增长
提及
频道
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频道帖子
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https://t.me/UdemySybot?start=ref_418788114 Get Free Courses 😁
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"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent" To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation. It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently. https://algebrica.org/learning-mathematics/
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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
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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"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one+1
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights. The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods. I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training. https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf https://t.me/CodeProgrammer 🤩
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If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning
If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning. This is not an advertisement: I personally used it and decided to share it with you. https://deep-ml.com 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 Visua
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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🤖 A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned. A residential proxy can help with this by routing your requests through IPs from different regions. For example, with Python: import requests proxies = { "http": "http://USER:PASSWORD@HOST:PORT", "https": "http://USER:PASSWORD@HOST:PORT" } response = requests.get( "https://example.com", proxies=proxies ) print(response.status_code) Replace USER, PASSWORD, HOST, and PORT with your proxy credentials. 🚀 711Proxy provides real residential IPs across 200+ countries and regions, with SOCKS5 support and sticky sessions — useful for data collection, testing, and other location-based ML workflows. 🎁 1GB free for testing New users can use 711TRIAL to get 1GB of residential proxy traffic. 👉 https://www.711proxy.com After registration, contact 711Proxy support and mention “711TRIAL” to claim the trial. Available to eligible new users.
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