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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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Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertRed:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertIndia4 219Educación2 107

📈 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 75 837 suscriptores, ocupando la posición 2 107 en la categoría Educación y el puesto 4 219 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 837 suscriptores.

Según los últimos datos del 22 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 728, 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 3.00%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.05% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 278 visualizaciones. En el primer día suele acumular 794 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 23 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.

75 837
Suscriptores
-224 horas
+637 días
+72830 días
Archivo de publicaciones
Data Science & Machine Learning
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Machine learning using Python

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Top 50 Machine Learning Interview Q&A.pdf2.61 KB

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Lynda.com - Data Science Foundations - Fundamentals.zip665.81 MB

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Free ML webinar to learn how Swiggy uses Data Science! Link: https://bit.ly/3gNRBy0 ✅ Only for Indian users
Free ML webinar to learn how Swiggy uses Data Science! Link: https://bit.ly/3gNRBy0 ✅ Only for Indian users

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Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words? Usually logistic regression is better because bag of words creates a matrix with large number of columns. For a huge number of columns logistic regression is usually faster than gradient boosting trees.

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MathforML.pdf5.00 MB

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Do you have a business idea? We are waiting for you! We are the first international channel PitchCamp, which prepared for you
Do you have a business idea? We are waiting for you! We are the first international channel PitchCamp, which prepared for you: ✔️Weekly opportunity to win $500 - $5000 just for business idea ✔️Daily educational tools & analytics ✔️Daily advises from our experts ✔️Live Performance from our active startups ✔️Up to $150.000 from our community for real start up Make the first step, get information and apply for weekly contest. 👉 PitchCamp

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Python Programming. Python Programming for Beginners, Python Programming for Intermediates

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Artificial Neural Networks with Java

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LinkedIn - Python for Data Science Essential Training Part 2.zip390.08 MB

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🌐 Join the researchers and programmers channel (Courses, Books, Papere and Codes). t.me/DataScience_Books

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Advanced ML with Python

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Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications - Laura Igual, Santi Segui (Springer, 2017)

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What is unsupervised learning? Unsupervised learning aims to detect patterns in the data where no labels are given.

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Tableau_Cheatsheet.pdf1.65 KB

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git-cheat-sheet-education.pdf0.98 KB

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What are precision, recall, and F1-score? Precision and recall are classification evaluation metrics: P = TP / (TP + FP) and R = TP / (TP + FN). Where TP is true positives, FP is false positives and FN is false negatives In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives. F1 is a combination of both precision and recall in one score (harmonic mean): F1 = 2 * PR / (P + R). Max F score is 1 and min is 0, with 1 being the best.

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How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bi
How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bit.ly/2SLpFlw

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Machine Learning God by Stefan Stavrev (version 2.0).pdf1.57 MB

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