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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 254Educación2 109

📈 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 75 822 suscriptores, ocupando la posición 2 109 en la categoría Educación y el puesto 4 254 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 822 suscriptores.

Según los últimos datos del 20 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 833, y en las últimas 24 horas de 1, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.15%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.15% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 391 visualizaciones. En el primer día suele acumular 875 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 21 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.

75 822
Suscriptores
+124 horas
+1047 días
+83330 días
Archivo de publicaciones
Data Science & Machine Learning
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Maths for Data-science Notes.pdf10.11 KB

Data Science & Machine Learning
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10 mindblowing tricks revoling around f-strings.pdf0.63 KB

Data Science & Machine Learning
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🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at o
🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/ll6bk, make your choice and apply now while there are still seats available. See you there! ▶️ March 13 - Tech job for Beginners: Become a Software Tester. Free Webinar ▶️ March 14 - How to Become a UX Designer: Online Training for Everyone. Free Webinar ▶️ March 14 - Manual QA. First Free Lesson ▶️ March 15 - Tech Support. First Free Lesson ▶️ March 15 - How to Become a Sales Engineer: Online Training for Everyone. Free Webinar ▶️ March 16 - UX Design. First Free Lesson ▶️ March 20 - Sales Engineering. First Free Lesson Special offer for all participants! ️✅ Apply by the link https://crst.co/ll6bk

Data Science & Machine Learning
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7 Baby steps to start with Machine Learning: 1. Start with Python 2. Learn to use Google Colab 3. Take a Pandas tutorial 4. Then a Seaborn tutorial 5. Decision Trees are a good first algorithm 6. Finish Kaggle's "Intro to Machine Learning" 7. Solve the Titanic challenge

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Understanding Deep Learning Simon J.D. Prince, 2023

Data Science & Machine Learning
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Applied Machine Learning and AI for Engineers Jeff Prosise, 2023

Data Science & Machine Learning
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SQL Cheat Sheet.pdf4.72 MB

Data Science & Machine Learning
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Docker for Data Scientists (2).pdf1.77 MB

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Data Science resources.pdf2.32 KB

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1. What are the disadvantages of the linear regression model? One of the most significant demerits of the linear model is that it is sensitive and dependent on the outliers. It can affect the overall result. Another notable demerit of the linear model is overfitting. Similarly, underfitting is also a significant disadvantage of the linear model. 2. Why Naive Bayes is called Naive? We call it naive because its assumptions (it assumes that all of the features in the dataset are equally important and independent) are really optimistic and rarely true in most real-world applications: we consider that these predictors are independent we consider that all the predictors have an equal effect on the outcome (like the day being windy does not have more importance in deciding to play golf or not) 3. How does Random Forest handle missing values? The Random Forest methods encourage two ways of handling missing values: Drop data points with missing values. This is not recommended due to the fact that all the available data points is not used. Fill in the missing values with the median (for numerical values) or mode (for categorical values). This method will brush too broad a stroke for datasets with many gaps and significant structure. There are other methods of filling in missing values such as calculating the similarity between the missing features, and the missing values estimated by weighting. 4. Why does XGBoost perform better than SVM? In case of missing values, XGB is internally designed to handle missing values. The missing values are interpreted in such a way that if there endures any trend in the missing values, it is captured by the model. Users are required to supply a different value than other observations and pass that as a parameter. XGBoost tries different things as it encounters a missing value on each node and learns which path to take for missing values in future. On the other hand, Support Vector Machine (SVM) does not perform well with the missing data and it is always a better option to impute the missing values before running SVM. ENJOY LEARNING 👍👍

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Telegram bot for secure conversations: 🔸 Make phone calls worldwide; 🔸 Rent virtual numbers for incoming calls / SMS; 🔸 Pu
Telegram bot for secure conversations: 🔸 Make phone calls worldwide; 🔸 Rent virtual numbers for incoming calls / SMS; 🔸 Purchase eSIM and use mobile internet in 178 countries; 🔸 Pay with cryptocurrencies (BTC, ETH, TON, USDT, USDC, etc.); 🔸 Invite friends and Earn. Want to try it for free? Go to the bot and get a trial period 🎁

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The Python Quiz Book Michael Driscoll, 2022

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Probability for the enthusiastic beginner.pdf2.00 MB

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Data Science Interview Questions and Answers.pdf13.81 MB

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Python Data Science Handbook Jake VanderPlas, 2023

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Matplotlib Cheatsheets Matplotlib Development Team, 2021

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Probability for the enthusiastic beginner.pdf2.00 MB

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Maths for Data-science Notes.pdf10.11 KB

Data Science & Machine Learning
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PAIR BTC and USDT 🔁 Binance.com Buy -21,815$ 🔁 Bityx.us Sell - 22,670$ 💹Profit per BTC traded 921$ TG - https://t.me/arbitragebityx

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Machine Learning and AI Foundations.zip331.66 MB