متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Data Science & Machine Learning
الذهاب إلى القناة على Telegram
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data Science & Machine Learning
تُعد قناة Data Science & Machine Learning (@datasciencefun) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 75 833 مشتركاً، محتلاً المرتبة 2 106 في فئة التعليم والمرتبة 4 234 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 75 833 مشتركاً.
بحسب آخر البيانات بتاريخ 21 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 770، وفي آخر 24 ساعة بمقدار 8، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.15%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.09% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 385 مشاهدة. وخلال اليوم الأول يجمع عادةً 827 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, accuracy, distribution, panda, dataset.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 22 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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Which of the following tool can be used for data visualization?
Fundamentals_of_Deep_Learning,_2nd_Edition_Fourth_Early_Release.pdf10.94 MB
New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.
Don't get bogged down trying to learn every new term & technology you come across.
Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.
The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!
Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-to-end PDF
💥Deep Learning with Pytorch by Prof.Yann LeCun (CNN Founder)
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
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Top 8 Github Repos to Learn Data Science and Python
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Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals PDF
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Python for Data Science: The Ultimate Step-by-Step Guide to Learn Python In 7 Days & NLP, Data Science from with Python PDF
A LITTLE GUIDE TO HANDLING MISSING DATA
Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀
How can you handle these missing values, ensuring you dont loose important part of your data🤷♀️
Not a problem😌. Here are important facts you must know😉
✍️Instances with missing values for all features should be eliminated
✍️Features with high absence rate should either be eliminated or filled with values
✍️Missing values can be replaced using Mean Imputation or Regression Imputation
✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances
✍️Regression Imputation might overfit your model
✍️Mean and Regression Imputation can't be applied to Text features with missing values
✍️Text Features with missing values can be eliminated if not needed in data
✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized
