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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 77 616 suscriptores, ocupando la posición 2 020 en la categoría Educación y el puesto 3 811 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 77 616 suscriptores.

Según los últimos datos del 14 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -147, y en las últimas 24 horas de -20, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.41%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.97% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 872 visualizaciones. En el primer día suele acumular 755 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
  • 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 15 noviembre, 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.

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77 616
Suscriptores
-2024 horas
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-14730 días
Archivo de publicaciones
Data Science & Machine Learning
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Which function is used to randomly reorder a Series or rows in Dataframe?
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Which of the following is correct code to generate 20 normally distributed random numbers? import numpy as np
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Which type of data is separated into bins for analysis
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Just a correction here "np.nan" instead of "na.nan" Hope you all are aware that mostly we import numpy as np

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Which of the following is incorrect code to replace na values with 0 in data?
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Majority is correct Well done guys 👍👍 Scikit learn can be specially used to solve classification, regression, clustering and dimensionality reduction problems

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Which Scipy package can be used to solve differential equations?
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Which Scipy package can be used for standard continuous and discrete probability distributions?
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Keyboard shortcuts for data scientists

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Which of the following is not a function of scikit learn?
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An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science. Basically, there are 3 different layers in a neural network : Input Layer (All the inputs are fed in the model through this layer) Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers) Output Layer (The data after processing is made available at the output layer) Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

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Free Resources for Hacking and Programming 👇👇 https://t.me/joinchat/ZYXe60w7YisyNWEx

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Top Bayesian Algorithms and Methods: - Naive Bayes. - Averages one-dependence estimators. - Bayesian belief networks. - Gaussian naive Bayes. - Multinomial naive Bayes. - Bayesian networks.

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SQL-cheat-sheet.pdf2.25 KB

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SQL Cheetsheet

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Data science Bootcamp with 5 projects 👇👇 https://www.udemy.com/course/data-science-bootcamp-with-python/?couponCode=FREE_UDS [Free for limited time]

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Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining