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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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Red: Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpRed: Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertIndia3 829Educación2 010

📈 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 77 693 suscriptores, ocupando la posición 2 010 en la categoría Educación y el puesto 3 829 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 77 693 suscriptores.

Según los últimos datos del 09 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -87, y en las últimas 24 horas de -6, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.23%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.99% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 512 visualizaciones. En el primer día suele acumular 770 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
  • 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 10 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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77 693
Suscriptores
-624 horas
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Archivo de publicaciones
Data Science & Machine Learning
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Machine Learning with Python.pdf28.13 MB

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Probability Distributions Cheat Sheet.pdf2.57 MB

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Hands-On Graph Neural Networks Using Python.pdf35.45 MB

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🔰 Learning Python for Data Analysis and Visualization ⏱ 21 Hours 📦 110 Lessons Learn python and how to use it to analyze,vi
🔰 Learning Python for Data Analysis and Visualization ⏱ 21 Hours 📦 110 Lessons Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video! Taught By: Jose Portilla Download Full Course: https://t.me/pythonanalyst/26 Download All Courses: https://t.me/DataAnalystInterview

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Natural Language Processing with Transformers.pdf17.27 MB

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Python Automation Cookbook Jaime Buelta, 2020

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Practical Statistics for Data Scientists.pdf15.98 MB

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R for Data Science.pdf19.76 MB

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1. What are the different subsets of SQL? Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects. Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database. Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions. 2. List the different types of relationships in SQL. There are different types of relations in the database: One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other. One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another. Many-to-Many – This is used when defining a relationship that requires several instances on each sides. Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ. 3. How to create empty tables with the same structure as another table? To create empty tables: Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active. 4. What is Normalization and what are the advantages of it? Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are: Better Database organization More Tables with smaller rows Efficient data access Greater Flexibility for Queries Quickly find the information Easier to implement Security

Data Science & Machine Learning
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PYTHON_DATA_SCIENCE_ESSENTIALS_THIRD_EDITION @computer_books.pdf6.63 MB

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Statistics Slam Dunk.pdf8.97 MB

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Electrical Machine Fundamentals with Numerical Simulation using MATLAB/SIMULINK Atif Iqbal, 2021

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Stack Overflow jumps into the generative AI world with OverflowAI

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Natural Language Processing in the Real World.pdf25.62 MB

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Friendly Introduction to Numerical Analysis Brian Bradie, 2006

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Siemens is hiring Data Analyst/ Data Engineer! https://t.me/getjobss/1445

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The Pragmatic Programmer for Machine Learning.pdf7.68 MB

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Python Programming Notes 📝

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Practical Guide to Scikit-Learn for Data Science.pdf9.22 KB