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:📈 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 68 138 名订阅者，在 教育 类别中位列第 2 366，并在 印度 地区排名第 4 740 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 68 138 名订阅者。
根据 30 八月, 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 86，过去 24 小时变化为 27，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 4.07%。内容发布后 24 小时内通常能获得 1.52% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 775 次浏览，首日通常累积 1 037 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 5。
- 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 31 八月, 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。
68 138
订阅者
+2724 小时
-297 天
+8630 天
帖子存档
Big surprise in our channels on Discord
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Repost from Machine Learning
📌 PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks
🗂 Category: DEEP LEARNING
🕒 Date: 2025-09-24 | ⏱️ Read time: 15 min read
Deep learning is shaping our world as we speak. In fact, it has been slowly…
Most of the time, you regret helping others in general, because not everyone appreciates your hard work or the effort it takes you to spread a piece of information, even if it's quoted
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Repost from Machine Learning
📌 Paper Walkthrough: Attention Is All You Need
🗂 Category: DEEP LEARNING
🕒 Date: 2024-11-03 | ⏱️ Read time: 46 min read
The complete guide to implementing a Transformer from scratch
Repost from Machine Learning
📌 Building a Convolutional Neural Network (CNNs) from Scratch
🗂 Category:
🕒 Date: 2024-11-05 | ⏱️ Read time: 15 min read
Line-by-Line, Let’s Build a ResNet Classifier on the MNIST-Fashion Dataset
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💯 Use Kaggle like a pro with this method!
👨🏻💻 Never underestimate Kaggle! One of the best ways to start learning data science and ML is Kaggle. A place where theory turns into practice, beginners become professionals, and skills turn into value.
🎯 This roadmap is the key to practical use of this amazing platform:👇
⬅️ Step one: Strengthen your basic skills!
✏️ Start with Kaggle's short and free courses. Practical, focused, and suitable for beginners.
✅ Python ⬅️ Link
☑️ Introduction to Machine Learning ⬅️ Link
✔️ Introduction to Deep Learning ⬅️ Link
✔️ Introduction to SQL ⬅️ Link
✔️ Introduction to Game AI and RL ⬅️ Link
📝 Complete list of courses ⬅️Link
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⬅️ Step two: Apply what you’ve learned.
✏️ Learning alone is not enough; you have to solve problems! Kaggle competitions are the best place for this.
✅ Classification problem for beginners
☑️ Regression-based challenge
✔️ Fake news detection with NLP
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📝 Complete list of competitions ⬅️Link
https://t.me/CodeProgrammer 🌟
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👨🏻💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
✅ Why should we use it?
🔢 For learning: If you're looking to learn algorithms in action, this is great.
🔢 For practice: You can take the codes, run them, and modify them to better understand.
🔢 For projects : You can even use the codes here in real-life or academic projects.
🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
┌ 🏳️🌈 The Algorithms - Python
└ 🐱 GitHub-Repos
