اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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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کانال DataSpoof (@dataspoof) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 16 134 مشترک است و جایگاه 12 546 را در دسته آموزش و رتبه 26 595 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 16 134 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 21 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -143 و در ۲۴ ساعت گذشته برابر -2 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
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- دسترسی پستها: هر پست به طور میانگین 0 بازدید دریافت میکند. در اولین روز معمولاً 0 بازدید جمعآوری میشود.
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https://dataspoof4081.graphy.com/membership
Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 22 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
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آرشیو پست ها
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Some of the links will not be working
https://medium.com/coders-camp/230-machine-learning-projects-with-python-5d0c7abf8265
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How to build a website using chatgpt
https://www.instagram.com/p/CqsnrcXPMYx/?igshid=YmMyMTA2M2Y=
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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
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What are the various time series algorithms available for forecasting
Source- Instagram
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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.
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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.
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How to data preprocessing speed using polar library.
Polar is a powerful data preprocessing library which support parallel processing.
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