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Machine Learning with Python

Machine Learning with Python

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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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📈 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 67 813 مشترک است و جایگاه 2 427 را در دسته آموزش و رتبه 5 028 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 67 813 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 13 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 60 و در ۲۴ ساعت گذشته برابر -3 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 4.31% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.69% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 2 926 بازدید دریافت می‌کند. در اولین روز معمولاً 1 148 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 6 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 14 ژوئن, 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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Learn PyTorch in 4 steps 👨🏻‍💻 With support for more than 200 different mathematical operations, PyTorch is one of the most powerful open source and computational libraries based on Python for machine learning. You don't need to look for different sources to learn this popular library, but the website of this library covers more than what you need.💯 2️⃣ Here, I will teach you how to learn PyTorch from scratch in 4 steps. 👌🏼 1️⃣ Text tutorial: If you don't know anything about Python, you should start learning from here. ⬅️ Topics: tensors, datasets and DataLoader, model building, optimization loop, saving, loading and using the model. 📄 PyTorch Basics 📎 Link: Learn the Basics 2️⃣ Video training: This section is very useful for those who prefer video content. ⬅️ Topics: introduction, tensor construction and model construction with pytorch. 📄 Introduction to PyTorch 📎 Link: Introduction to PyTorch 3️⃣ Examining examples: By viewing different examples, you will review what you have learned so far. 📄 Learning Python by example 📎 Link: Learning PyTorch with Examples 4️⃣ Coding: This step is vital! Your theoretical knowledge is worthless without coding! The Keras website is full of sample code that is great for getting started. 📄 Start coding 📎 Link: keras.io

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