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Data Science & Machine Learning

Data Science & Machine Learning

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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

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📈 Análisis del canal de Telegram Data Science & Machine Learning

El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 822 suscriptores, ocupando la posición 2 109 en la categoría Educación y el puesto 4 254 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 822 suscriptores.

Según los últimos datos del 20 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 833, y en las últimas 24 horas de 1, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.15%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.15% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 391 visualizaciones. En el primer día suele acumular 875 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
  • Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
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

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 21 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.

75 822
Suscriptores
+124 horas
+1047 días
+83330 días
Archivo de publicaciones
+2
Deep Learning Applications 2 M. Arif Wani, 2021

The Data Science Handbook Field Cady, 2017

Data Science Interview Questions and Answers 👨‍💻.pdf13.81 MB

The Data Science Handbook Carl Shan, 2015

ML_Projects_270.pdf3.69 KB

devops-1.pdf1.91 MB

Pandas loc & iloc Function.pdf0.50 KB

SparkNotes.pdf2.30 KB

+1
Foundational Python for Data Science.pdf26.26 MB

🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at o
🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/Dxfog, make your choice and apply now while there are still seats available. See you there! ▶️ December 12 - Most In-Demand IT Jobs 2023: Become a Systems Engineer ▶️ December 13 - Tech Jobs for Beginners: Become a Software Tester ▶️ December 15 - Most In-Demand IT Jobs 2023: Become a Software Tester ▶️ January 5 - UX Design. First Free Lesson ▶️ January 9 - Sales Engineering. First Free Lesson Special offer for all participants! ️ ✅ Apply by the link https://crst.co/Dxfog 

An high level overview for becoming a machine learning engineer
An high level overview for becoming a machine learning engineer

Practical MLops.pdf1.69 MB

DATA CLEANING AND PROCESSING.pdf2.26 MB

Stats Notes 1.pdf4.06 MB

Cheatsheet Supervised Learning.pdf6.41 KB

What topic does AI cover
What topic does AI cover

Data Science Bookcamp Leonard Apeltsin, 2021

Deep Learning from Scratch Seth Weidman, 2019

1. What do you understand by the term silhouette coefficient? The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score. 2. What is the difference between trend and seasonality in time series? Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again. 3. What is Bag of Words in NLP? Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order. 4. What is the difference between bagging and boosting? Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm ENJOY LEARNING 👍👍

Hands on Plotly👍.pdf7.53 KB