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

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India4 219 Educación2 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

2 278

Visitas de la publicación

~ 79424 horas

~ 1 00048 horas

3.00%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Adaptive_Computation_and_Machine_Learning_Ralf_Herbrich_Learning.pdf2.69 MB

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Some interview questions related to Data science 1- what is difference between structured data and unstructured data. 2- what is multicollinearity.and how to remove them 3- which algorithms you use to find the most correlated features in the datasets. 4- define entropy 5- what is the workflow of principal component analysis 6- what are the applications of principal component analysis not with respect to dimensionality reduction 7- what is the Convolutional neural network. Explain me its working

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👋 Welcome to @datastructuresandalgoofficial 👋 To get job in top tech companies like 𝙂𝙤𝙤𝙜𝙡𝙚, 𝘼𝙢𝙖𝙯𝙤𝙣, 𝙈𝙞𝙘𝙧𝙤𝙨𝙤𝙛𝙩, 𝙁𝙖𝙘𝙚𝙗𝙤𝙤𝙠, 𝙚𝙩𝙘 you must know 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨. And for excelling in tech field you should know 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨. In this Channel you will get all materials related Data Structures and Algorithms(DSA). 𝙁𝙤𝙧 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨(𝘿𝙎𝘼) 𝙛𝙤𝙧 𝙛𝙧𝙚𝙚 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡: https://t.me/datastructuresandalgoofficial So what are you waiting for? Join right now👍 https://t.me/datastructuresandalgoofficial

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Free Data Science courses from Udemy and Udacity 👇👇 Intro to Data Science https://imp.i115008.net/rn2beD Data Analysis and Visualization https://imp.i115008.net/JrBjZR Data Analysis with R by Facebook https://imp.i115008.net/gbJr5r Introduction to Data Science using Python https://ern.li/OP/1qvkxbfaxqj Intro to Data for Data Science https://ern.li/OP/1qvkxbfbmf8 Data Science with Analogies, Algorithms and Solved Problems https://ern.li/OP/1qvkxbfcehz Introduction to Data Science for Complete Beginners https://bit.ly/3sh4oPO ENJOY LEARNING 👍👍

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https://plotly.com/python/bar-charts/

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Source codes for data science projects 👇👇 1. Build chatbots: https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro 2. Credit card fraud detection: https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python 3. Fake news detection https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/ 4.Driver Drowsiness Detection https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/ 5. Recommender Systems (Movie Recommendation) https://data-flair.training/blogs/data-science-r-movie-recommendation/ 6. Sentiment Analysis https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/ 7. Gender Detection & Age Prediction https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/ 𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍

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python web frameworks.epub1.88 MB

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hadoop with python.pdf1.75 MB

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Digital transformation started a shift to remote work and so did the conferences. All major events in IT conferencing world now have online block or are fully online.Stay tuned and never miss a major event in IT conferencing world with https://t.me/getconf. Upcoming online and offline conferences announcements, various themes, ticket pricing and more!

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Python Crash Course – Ole Nielsen (en).pdf1.73 KB

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20 python libraries you arent using but should.pdf4.13 MB

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Data Science from Scratch- First Principles with Python.pdf5.57 MB

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Practical_Machine_Learning_and_Image_Processing_For_Facial_Recognition.pdf4.78 MB

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wiley_a_practical_introduction_to_computer_vision_with_opencv_2014.pdf12.95 MB

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OpenCV_Computer_Vision_with_Python_Learn_to_capture_videos,_manipulate.pdf3.74 MB

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Machine_Learning_Step_by_Step_Guide_To_Implement_Machine_Learning.pdf1.58 MB

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Machine_Learning_with_Python_Cookbook_Practical_Solutions_from_Preprocessing.pdf4.59 MB

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dive-into-python3.pdf2.49 MB