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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 94 048 مشتركاً، محتلاً المرتبة 1 561 في فئة التعليم والمرتبة 3 020 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 94 048 مشتركاً.

بحسب آخر البيانات بتاريخ 24 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 986، وفي آخر 24 ساعة بمقدار 67، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 6.50‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.56‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 6 109 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 470 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 8.
  • 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 25 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

94 048
المشتركون
+6724 ساعات
+1517 أيام
+98630 أيام
أرشيف المشاركات
🔗 Roadmap to master Machine Learning
+4
🔗 Roadmap to master Machine Learning

🔗 Roadmap to master Machine Learning
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🔗 Roadmap to master Machine Learning

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🔗 The Only roadmap you need to become an ML Engineer 🥳 Phase 1: Foundations (1-2 Months) 🔹 Math & Stats Basics – Linear Algebra, Probability, Statistics 🔹 Python Programming – NumPy, Pandas, Matplotlib, Scikit-Learn 🔹 Data Handling – Cleaning, Feature Engineering, Exploratory Data Analysis Phase 2: Core Machine Learning (2-3 Months) 🔹 Supervised & Unsupervised Learning – Regression, Classification, Clustering 🔹 Model Evaluation – Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC) 🔹 Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization 🔹 Basic ML Projects – Predict house prices, customer segmentation Phase 3: Deep Learning & Advanced ML (2-3 Months) 🔹 Neural Networks – TensorFlow & PyTorch Basics 🔹 CNNs & Image Processing – Object Detection, Image Classification 🔹 NLP & Transformers – Sentiment Analysis, BERT, LLMs (GPT, Gemini) 🔹 Reinforcement Learning Basics – Q-learning, Policy Gradient Phase 4: ML System Design & MLOps (2-3 Months) 🔹 ML in Production – Model Deployment (Flask, FastAPI, Docker) 🔹 MLOps – CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow) 🔹 Cloud & Big Data – AWS/GCP/Azure, Spark, Kafka 🔹 End-to-End ML Projects – Fraud detection, Recommendation systems Phase 5: Specialization & Job Readiness (Ongoing) 🔹 Specialize – Computer Vision, NLP, Generative AI, Edge AI 🔹 Interview Prep – Leetcode for ML, System Design, ML Case Studies 🔹 Portfolio Building – GitHub, Kaggle Competitions, Writing Blogs 🔹 Networking – Contribute to open-source, Attend ML meetups, LinkedIn presence The data field is vast, offering endless opportunities so start preparing now.

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🔅 08 - FineTuning LLMs

🔅 07 - LLM Context Window

🔅 06 - LLM Parameters

🔅 05 - Inside an LLM Under the Hood

🔅 04 - How LLMs generate text

🔅 03 - Who invented LLMs

🔅 02 - Ingredients of an LLM

🔅 01 - Introduction to LLMs

LLMs Intuition 01 - Introduction to LLMs 02 - Ingredients of an LLM 03 - Who invented LLMs 04 - How LLMs generate text 05 - Inside an LLM Under the Hood 06 - LLM Parameters 07 - LLM Context Window 08 - FineTuning LLMs

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📦 Exercise Files