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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
Machine Learning with Pythonشبکه:Machine Learning with Pythonالهند5 047آموزش2 412

📈 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 67 810 مشترک است و جایگاه 2 412 را در دسته آموزش و رتبه 5 047 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 67 810 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 08 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 50 و در ۲۴ ساعت گذشته برابر -5 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 2.79% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.60% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 1 895 بازدید دریافت می‌کند. در اولین روز معمولاً 1 764 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 7 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 09 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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Machine Learning with Python
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“Learn AI” is everywhere. But where do the builders actually start? Here’s the real path, the courses, papers and repos that matter. ✅ Videos: Everything here ⇒ https://lnkd.in/ePfB8_rk ➡️ LLM Introduction → https://lnkd.in/ernZFpvB ➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn ➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq ➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW ➡️ Building Effective Agents → https://lnkd.in/eqxvBg79 ➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K ➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn ✅ Courses: All Courses here ⇒ https://lnkd.in/eKKs9ves ➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE ➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP ➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs ➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs ➡️ Agent Memory → https://lnkd.in/egC8h9_Z ➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa ➡️ Building Browser Agents → https://lnkd.in/ewy3sApa ➡️ LLMOps → https://lnkd.in/ex4xnE8t ➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW ➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU ➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR ➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M ➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9 ➡️ Multi Agent Systems → https://lnkd.in/evBnavk9 ✅ Guides: Access all ⇒ https://lnkd.in/e-GA-HRh ➡️ Google's Agent → https://lnkd.in/encAzwKf ➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg ➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ ➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju ➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh ✅ Repos: ➡️ GenAI Agents → https://lnkd.in/eAscvs_i ➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic ➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP ➡️ AI Agent Papers → https://lnkd.in/esMHrxJX ✅ Papers: 🟡 ReAct → https://lnkd.in/eZ-Z-WFb 🟡 Generative Agents → https://lnkd.in/eDAeSEAq 🟡 Toolformer → https://lnkd.in/e_Vcz5K9 🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq 🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S 🟡 Reflexion → https://lnkd.in/eggND2rZ 🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE Access all ⇒ https://lnkd.in/e-GA-HRh By: https://t.me/CodeProgrammer 🟡

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Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM I spent years chasing success until I found the 7 daily habits no one talks about—now everything’s changed for me. Most people miss the real secret. See what you’ve been overlooking: Success Tips 🔥 | InsideAds

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resp = RetinaFace.detect_faces("img1.jpg")
print(resp)

{
    "face_1": {
        "score": 0.9993440508842468,
        "facial_area": [155, 81, 434, 443],
        "landmarks": {
          "right_eye": [257.82974, 209.64787],
          "left_eye": [374.93427, 251.78687],
          "nose": [303.4773, 299.91144],
          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
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}
👉 @DataScienceN

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pip install qrcode pillow
import qrcode
from PIL import Image

data = input("Enter data for QR: ")
qr = qrcode.QRCode(version=3, box_size=8, border=4)
qr.add_data(data)
qr.make(fit=True)

image = qr.make_image(fill="black", back_color="aqua")
image.save("qr_code.png")
Image.open("qr_code.png")
The output is a ready QR code with any text or link. You can change colors, sizes, and style to fit your design 🙂 👉 https://t.me/CodeProgrammer

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