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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

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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 67 815 مشترک است و جایگاه 2 419 را در دسته آموزش و رتبه 5 033 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 67 815 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 12 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 57 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 4.13% 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 2 799 بازدید دریافت می‌کند. در اولین روز معمولاً 1 149 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 13 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.

67 815
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+324 ساعت
-187 روز
+5730 روز
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Data Science Cheat Sheets Quick help to make a data scientist's life easier ✅ http://t.me/codeprogrammer 🔒 💡 #deeplearning #AI #ML #python

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Data Science Cheat Sheets Quick help to make a data scientist's life easier About Dataset A collection of cheat sheets for va
Data Science Cheat Sheets Quick help to make a data scientist's life easier About Dataset A collection of cheat sheets for various data-science related languages and topics http://t.me/codeprogrammer 🔒 💡 #deeplearning #AI #ML #python

Andrew Ng just released two new AI Python courses for beginners! The course teaches how to write code using AI. If you're thi
Andrew Ng just released two new AI Python courses for beginners! The course teaches how to write code using AI. If you're thinking about learning to code, now is the perfect time to do so. https://deeplearning.ai/short-courses/ai-python-for-beginners/ http://t.me/codeprogrammer 🔒 💡 #deeplearning #AI #ML #python

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القناة دى قمة فى الروعة في البرمجة وفيها حوالى 40 دورة انصحكوا تشتركوا فيها 👏💙💞 https://www.youtube.com/channel/UCGbrg29FWhK503HN0KsPkjA?sub_confirmation=1 ودا جروب تليجرام تقدر تحصل فيه كورسات برمجية فى اى مجال حرفيا https://t.me/CISArab لو انت متخصص فى تراك ال PHP Laravel دا جروب رائع https://t.me/phpdevelopers2024 اما لو متخصص فى ال .Net Core فدا جروب عليه مشاريع كبيرة جدا https://t.me/C_Sharp_Developers اما لو بتحب البايثون https://t.me/learncsharp_programing ودا فيديو ازاى تقدر تكسب فلوس وانت طالب https://youtu.be/aqTdGNm9tcg

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Building Agents: Free Course We just released a course with > 20 videos & notebooks focused on building agents. All code is o
Building Agents: Free Course We just released a course with > 20 videos & notebooks focused on building agents. All code is open-source and the course is free! Context Back in June, I gave at talk at @aiDotEngineer on building agents with LangGraph. I got ~2 hrs of questions. We took these questions along with lots of feedback we've heard from users and built a course! Module 1: Foundations The first module includes several notebooks & videos that focus on what is an agent explained in simple terms, how to build various types of agents (routers, ReAct, etc), how to debug them w LangGraph Studio, and how to deploy them w LangGraph Cloud. Module 2: Memory One of the biggest questions we've heard is how to build long-running agents, which can remember important details. We show how memory works with LangGraph, and how to use various databases (SQLite, Postgres) to serve as agent memory. Module 3: Human-In-The-Loop Another central question with agents is allowing humans to approve actions (tools use) or modify the agent state (add feedback). We show various human in the loop interaction patterns that are supported in LangGraph, and also show how to stream the graph state during agent execution for human review. Module 4: Controllability The final module focuses on various design patterns for agent control flow, including parallelization of tasks and creating multi-agent teams with their own tasks / internal memory. This builds up into a customizable multi agent system for research that pulls together themes from the entire course. Course (links to code, all videos): https://academy.langchain.com/courses/intro-to-langgraph http://t.me/codeprogrammer 🔒 💡 #deeplearning #AI #ML #python

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New course with @Intel! Multimodal RAG: Chat with Videos is available starting today! Learn from @Vasudev_Lal to build an AI chat system that answers questions from video content! Learn to: 📷 Create embeddings from videos 🧩 Build a RAG pipeline for data retrieval 💬 Use Large Vision-Language Models (LVLMs) for Q&A using both text and image inputs. In this course, you will make API calls to access multimodal models hosted by @PredictionGuard on Intel’s cloud. Enroll Free Here: https://www.deeplearning.ai/short-courses/multimodal-rag-chat-with-videos/ http://t.me/codeprogrammer 🔒 💡 #deeplearning #AI #ML #python

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Convert CSV to JSON with Python ⭐️ Github http://t.me/codeprogrammer 🔒 🔒 #deeplearning #AI #ML #python

[Coursera] Deep Learning Specialization What you’ll learn • Build and train deep neural networks, identify key architecture p
[Coursera] Deep Learning Specialization What you’ll learn • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering Specialization – 5 course series 1. Neural Networks and Deep Learning 2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 3. Structuring Machine Learning Projects 4. Convolutional Neural Networks 5. Sequence Models Available free in our Paid Channel https://t.me/+qvMSLM70zys1ZTJi

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