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Data Science & Machine Learning

Data Science & Machine Learning

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The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. For promotions: @love_data

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📈 Análisis del canal de Telegram Data Science & Machine Learning

El canal Data Science & Machine Learning (@datascienceinterviews) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 27 227 suscriptores, ocupando la posición 7 209 en la categoría Educación y el puesto 16 024 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 27 227 suscriptores.

Según los últimos datos del 10 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 103, y en las últimas 24 horas de 7, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.78%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.62% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 212 visualizaciones. En el primer día suele acumular 170 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
  • Intereses temáticos: El contenido se centra en temas clave como insidead, mining, pinix, learning, neo.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. For promotions: @love_data

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 11 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.

27 227
Suscriptores
+724 horas
+127 días
+10330 días
Archivo de publicaciones
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Miss the next market move? 🚨 5% Yield on 30-Year Treasuries may trigger a massive shift! Get real-time updates NOW on Tania
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A-Z of essential data science concepts A: Algorithm - A set of rules or instructions for solving a problem or completing a task. B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently. C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics. D: Data Mining - The process of discovering patterns and extracting useful information from large datasets. E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance. F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance. G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively. H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data. I: Imputation - The process of replacing missing values in a dataset with estimated values. J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously. K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups. L: Logistic Regression - A statistical model used for binary classification tasks. M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time. N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks. O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points. P: Precision and Recall - Evaluation metrics used to assess the performance of classification models. Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data. R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables. S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks. T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations. U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes. V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets. W: Weka - A popular open-source software tool used for data mining and machine learning tasks. X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks. Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters. Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊

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