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DataSpoof

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Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data

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📈 نظرة تحليلية على قناة تيليجرام DataSpoof

تُعد قناة DataSpoof (@dataspoof) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 16 139 مشتركاً، محتلاً المرتبة 12 546 في فئة التعليم والمرتبة 26 595 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 16 139 مشتركاً.

بحسب آخر البيانات بتاريخ 21 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -143، وفي آخر 24 ساعة بمقدار -2، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 7.89‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً N/A‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 0 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 0.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل api, llm, pipeline, +9183182, engineer.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big 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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أرشيف المشاركات
DataSpoof
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DataSpoof
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photo content

DataSpoof
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217 Machine Learning Projects with Python Code.pdf1.66 MB

DataSpoof
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The links of code is also in pdf

DataSpoof
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photo content

DataSpoof
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DataSpoof
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Data Engineering course 1. Master Python: https://lnkd.in/e5rCbvP8 2. Learn SQL: https://lnkd.in/efMKFkfX 3. Learn MySQL: https://lnkd.in/efk-Mi3c 4. Learn MongoDB: https://lnkd.in/eMKPWtqX 5. Dominate PySpark: https://lnkd.in/exwA2hKz 6. Learn Bash, Airflow & Kafka: https://lnkd.in/eyN6u2yd 7. Learn Git & GitHub: https://lnkd.in/eX_Q8s99 8. Learn CICD basics: https://lnkd.in/epKGivFY 9. Decode Data Warehousing: https://lnkd.in/eKnVbFAB 10. Learn DBT: : https://lnkd.in/eG9eaEuE 11. Learn Data Lakes: https://lnkd.in/eQ9xxAJT 12. Learn DataBricks: https://lnkd.in/ePZpCv86 13. Learn Azure Databricks: https://lnkd.in/eBij4akJ 14. Learn Snowflake: https://lnkd.in/erETmtFU 15. Learn Apache NiFi: http://bit.ly/43btwYy 16. Learn Debezium: http://bit.ly/3K6W5gL

DataSpoof
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https://www.instagram.com/p/CqktXUrNeA_/?igshid=YmMyMTA2M2Y= Follow us on Instagram for more data science related contents an
https://www.instagram.com/p/CqktXUrNeA_/?igshid=YmMyMTA2M2Y= Follow us on Instagram for more data science related contents and giveways

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List of popular ai tools
List of popular ai tools

DataSpoof
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Generative AI timeline Credit- David
Generative AI timeline Credit- David

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What are the various time series algorithms available for forecasting Source- Instagram www.instagram.com/dataspoof
What are the various time series algorithms available for forecasting Source- Instagram www.instagram.com/dataspoof

DataSpoof
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Various types of test used in statistics for data science T-test: used to test whether the means of two groups are significantly different from each other. ANOVA: used to test whether the means of three or more groups are significantly different from each other. Chi-squared test: used to test whether two categorical variables are independent or associated with each other. Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables. Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other. Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other. Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other. Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.

DataSpoof
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How to get GpU class performance on your CPU LAPTOP
How to get GpU class performance on your CPU LAPTOP

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What is online machine learning Online machine learning. Abhishek Singh Online machine learning Online machine learning is a type of machine learning that involves updating a model continuously based on new data points as they become available. In contrast to batch learning, where the model is trained on a fixed dataset, online learning adapts to new data incrementally and in real-time. Online learning is particularly useful in scenarios where data is constantly arriving and the model needs to be updated frequently to reflect the latest information. Examples include fraud detection, recommendation systems, and online advertising. In online learning, the model is initially trained on a small subset of the data, and as new data arrives, the model updates its parameters to incorporate the new information. The update process can be done using various algorithms, such as stochastic gradient descent or online gradient descent. Online learning has several advantages over batch learning, including the ability to adapt to changing data distributions, the ability to handle large datasets efficiently, and the ability to make real-time predictions. However, it also has some limitations, such as the need to carefully manage the learning rate to avoid overfitting, and the difficulty in handling non-stationary data streams.

DataSpoof
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How to data preprocessing speed using polar library. Polar is a powerful data preprocessing library which support parallel pr
How to data preprocessing speed using polar library. Polar is a powerful data preprocessing library which support parallel processing.

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Join and share our telegram channel with your friends to learn data science, machine learning, big data and , deep learning

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Docker for data scientists

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Docker for Data Scientists (2).pdf1.77 MB

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Empower your web-app with API.pdf1.22 MB

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Python DS question.pdf2.19 KB