Artificial Intelligence
الذهاب إلى القناة على Telegram
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
إظهار المزيد
📈 نظرة تحليلية على قناة تيليجرام Artificial Intelligence
تُعد قناة Artificial Intelligence (@machinelearning_deeplearning) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 56 625 مشتركاً، محتلاً المرتبة 3 064 في فئة التعليم والمرتبة 5 904 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 56 625 مشتركاً.
بحسب آخر البيانات بتاريخ 01 نوفمبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 423، وفي آخر 24 ساعة بمقدار 15، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 4.48%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.54% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 534 مشاهدة. وخلال اليوم الأول يجمع عادةً 874 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 13.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, classification, layer, pattern, chatbot.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 02 نوفمبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
56 625
المشتركون
+1524 ساعات
+737 أيام
+42330 أيام
أرشيف المشاركات
56 625
Here are the top 5 machine learning projects that are suitable for freshers to work on:
1. Predicting House Prices: Build a machine learning model that predicts house prices based on features such as location, size, number of bedrooms, etc. This project will help you understand regression techniques and feature engineering.
2. Image Classification: Create a model that can classify images into different categories such as cats vs. dogs, fruits, or handwritten digits. This project will introduce you to convolutional neural networks (CNNs) and image processing.
3. Sentiment Analysis: Develop a sentiment analysis model that can classify text data as positive, negative, or neutral. This project will help you learn natural language processing techniques and text classification algorithms.
4. Credit Card Fraud Detection: Build a model that can detect fraudulent credit card transactions based on transaction data. This project will help you understand anomaly detection techniques and imbalanced classification problems.
5. Recommendation System: Create a recommendation system that suggests products or movies to users based on their preferences and behavior. This project will introduce you to collaborative filtering and recommendation algorithms.
These projects will not only enhance your machine learning skills but also provide you with practical experience in working on real-world data science problems.
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56 625
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Book: 📚Exercises in Machine Learning
Authors: Michael U. Gutmann
year: 2024
pages: 211
56 625
Want to build AI Apps and get jobs in GenAI domain? 🚀
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🗓️ : 20th March || 9 PM
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✅ Working on Audio Models
✅ Text-to-Speech with ElevenLabs API
✅ Audio Transcription using Whisper Model
✅ Voice Cloning Techniques
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🚓 Deep Learning Roadmap 2024- Step-by-Step Career Path
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