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AI and Machine Learning

AI and Machine Learning

الذهاب إلى القناة على 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

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام AI and Machine Learning

تُعد قناة AI and Machine Learning (@machine_learning_courses) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 94 073 مشتركاً، محتلاً المرتبة 1 556 في فئة التعليم والمرتبة 3 013 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 94 073 مشتركاً.

بحسب آخر البيانات بتاريخ 25 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 981، وفي آخر 24 ساعة بمقدار 47، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 6.77‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.34‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 6 370 مشاهدة. وخلال اليوم الأول يجمع عادةً 2 203 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 9.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, llm, linkedin, linux, udemy.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 26 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

94 073
المشتركون
+4724 ساعات
+1877 أيام
+98130 أيام
أرشيف المشاركات
📱Artificial intelligence 📱Deep Learning Fundamentals for Healthcare

🔅 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

📖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

🤖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/

📦 Exercise Files

🔅 Deep Learning with Python: Optimizing Deep Learning Models 📝 Leverage techniques for optimizing deep learning models and
🔅 Deep Learning with Python: Optimizing Deep Learning Models 📝 Leverage techniques for optimizing deep learning models and implementing them using Python. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 2h 1m 📋 Topics: Deep Learning, Python 🔗 Join Artificial intelligence for more courses

🧠 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

🧠 RAG Algorithm You Must Implement
🧠 RAG Algorithm You Must Implement

🧠 RAG Cheat Sheet
🧠 RAG Cheat Sheet

Week 6 - Day 5.zip405.66 MB

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Week 6 - Day 4 - Part 01.zip490.81 MB

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Week 6 - Day 3 - Part 01.zip478.19 MB

Week 6 - Day 2.zip332.20 MB

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Week 6 - Day 1 - Part 01.zip494.45 MB

Week 🔢

Week 5 - Day 5.zip271.07 MB

Week 5 - Day 4.zip206.76 MB

Week 5 - Day 3.zip364.09 MB