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AI and Machine Learning

AI and Machine Learning

رفتن به کانال در Telegram

Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

نمایش بیشتر
الهند3 028آموزش1 568

📈 تحلیل کانال تلگرام AI and Machine Learning

کانال AI and Machine Learning (@machine_learning_courses) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 93 946 مشترک است و جایگاه 1 568 را در دسته آموزش و رتبه 3 028 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 93 946 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 23 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 993 و در ۲۴ ساعت گذشته برابر 92 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 7.92% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.62% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 7 435 بازدید دریافت می‌کند. در اولین روز معمولاً 1 526 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 9 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, llm, linkedin, linux, udemy تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses”

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 24 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.

93 946
مشترکین
+9224 ساعت
+1097 روز
+99330 روز
آرشیو پست ها
AI and Machine Learning
AI and Machine Learning
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🔅 Deep Learning Fundamentals for Healthcare 📝 Learn about deep learning in healthcare with this comprehensive course, inclu
🔅 Deep Learning Fundamentals for Healthcare 📝 Learn about deep learning in healthcare with this comprehensive course, including fundamentals, practical applications, advanced techniques, and more. 🌐 Author: Wuraola Oyewusi 🔰 Level: Intermediate ⏰ Duration: 2h 26m 📋 Topics: Healthcare Information Technology, Deep Learning, Computer Vision 🔗 Join Artificial intelligence for more courses

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📖We translate any PDF documents in one click 🛠 PDFMathTranslate is a free AI-powered tool for full-text translation of PDF documents. 🔰 Neural networks will translate books, articles, diagrams and graphs, preserving their presentable appearance 🔹 Works very quickly - even a 200-page article can be translated in a minute 🔹 Completely preserves the text layout and does not make phrases clumsy 🔹 Knows 10 languages 🔗Links: https://github.com/Byaidu/PDFMathTranslate

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Cloud Platform Models
Cloud Platform Models

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🤖Chat SDK 🛠 Chat SDK is a free, open-source template built with Next.js and the AI SDK that helps you quickly build powerfu
🤖Chat SDK 🛠 Chat SDK is a free, open-source template built with Next.js and the AI SDK that helps you quickly build powerful chatbot applications. ⚙️ Features 🔰Next.js App Router 🔹Advanced routing for seamless navigation and performance 🔹React Server Components (RSCs) and Server Actions for server-side rendering and increased performance 🔰AI SDK 🔹Unified API for generating text, structured objects, and tool calls with LLMs 🔹Hooks for building dynamic chat and generative user interfaces 🔹Supports xAI (default), OpenAI, Fireworks, and other model providers 🔰shadcn/ui 🔹Styling with Tailwind CSS 🔹Component primitives from 🔹Radix UI for accessibility and flexibility 🔰Data Persistence 🔹Neon Serverless Postgres for saving chat history and user data 🔹Vercel Blob for efficient file storage 🔰Auth.js 🔹Simple and secure authentication 🔗Links: https://github.com/vercel/ai-chatbot 🌐Site: https://chat.vercel.ai/

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📦 Exercise Files

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🧠 10 Machine Learning Concepts You Must Know ✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks ✅ Bias-Variance Tradeoff – Balance underfitting and overfitting ✅ Feature Engineering – The secret sauce to boost model performance ✅ Train-Test Split & Cross-Validation – Evaluate models the right way ✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1 ✅ Gradient Descent – The algorithm behind learning in most models ✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity ✅ Decision Trees & Random Forests – Interpretable and powerful models ✅ Support Vector Machines – Great for classification with clear boundaries ✅ Neural Networks – The foundation of deep learning

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🧠 RAG Algorithm You Must Implement
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🧠 RAG Cheat Sheet
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