DataSpoof

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India26 595 Educación12 546...

📈 Análisis del canal de Telegram DataSpoof

El canal DataSpoof (@dataspoof) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 16 134 suscriptores, ocupando la posición 12 546 en la categoría Educación y el puesto 26 595 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 16 134 suscriptores.

Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -143, y en las últimas 24 horas de -2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.89%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 0 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
Intereses temáticos: El contenido se centra en temas clave como api, llm, pipeline, +9183182, engineer.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 22 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

16 134

Suscriptores

-224 horas

-327 días

-14330 días

1 273

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

7.89%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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

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The links of code is also in pdf

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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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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

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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

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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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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.

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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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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