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Machine Learning with Python

Machine Learning with Python

رفتن به کانال در Telegram

Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

نمایش بیشتر

📈 تحلیل کانال تلگرام Machine Learning with Python

کانال Machine Learning with Python (@codeprogrammer) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 68 138 مشترک است و جایگاه 2 366 را در دسته آموزش و رتبه 4 740 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 68 138 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 30 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 86 و در ۲۴ ساعت گذشته برابر 27 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 4.07% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.52% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 2 775 بازدید دریافت می‌کند. در اولین روز معمولاً 1 037 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 5 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند insidead, learning, degree, evaluation, algorithm تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 31 اوت, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

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68 138
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+2724 ساعت
-297 روز
+8630 روز
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Repost from Machine Learning
📌 PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks 🗂 Category: DEEP LEARNING 🕒 Date: 2
📌 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…

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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 Th
📌 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

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📌 Building a Convolutional Neural Network (CNNs) from Scratch 🗂 Category: 🕒 Date: 2024-11-05 | ⏱️ Read time: 15 min read L
📌 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 sc
💯 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                    ➖➖➖➖➖➖ ⬅️ 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 ✔️ Deep learning on image data with TPU 📝 Complete list of competitions ⬅️Link https://t.me/CodeProgrammer 🌟

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Gradient Boosting for Regression Notes.pdf6.45 MB

👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python
👨🏻‍💻 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