¡No caigas en manos de tramposos! Telemetrio encuentra y marca estos canales 👉 Si quieres ver la etiqueta, suscríbete

Data Science & Machine Learning

Ir al canal en Telegram

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 ExpertIndia3 825Educación2 015...

📈 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 77 661 suscriptores, ocupando la posición 2 015 en la categoría Educación y el puesto 3 825 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 77 661 suscriptores.

Según los últimos datos del 12 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -123, y en las últimas 24 horas de -21, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.01% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 954 visualizaciones. En el primer día suele acumular 781 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 13 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.

Buy Ad

77 661

Suscriptores

-2124 horas

-767 días

-12330 días

1 954

Visitas de la publicación

~ 78124 horas

~ 1 03648 horas

2.52%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

Archivo de publicaciones

77 661

Numpy_Python_Cheat_Sheet.pdf6.49 KB

77 661

Want more free courses like this?

Anonymous voting

77 661

Data Visualisation Cheatsheet 🚀

77 661

9 Best Machine Learning Use cases in our Daily Lives 🚀 👓 Youtube Recommendation 👓 Voice Assistants 👓 arrow Smartphone Camera 👓 Google Maps routes 👓 Email Filtering 👓 Search 👓 Translation 👓 Chatbots 👓 Fraud Protection

77 661

Introduction_to_Machine_Learning_with_Python_A_Guide_for_Beginners.epub1.95 MB

77 661

Machine Learning CheatSheet.pdf3.44 MB

77 661

Machine Learning Cheatsheet #python #ml #cheatsheet #ai

77 661

🚀Join our first free lessons and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/imKOy, make your choice and apply now while there are still seats available. See you there! ▶️ May 4 - Manual QA Course. Free first lesson! ▶️ May 10 - Sales Engineer Course. Free first lesson!▶️ May 23 - Tech Sales Training. Free first lesson! ▶️ June 2 - Systems Engineer. Free first lesson! Special offer for all participants! ️✅ Apply by the link https://crst.co/hHZ9a

77 661

Repost from Machine Learning & Artificial Intelligence | Data Science Free Courses

TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

77 661

Machine Learning Notes - TutorialsDuniya.pdf14.65 MB

77 661

You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why? Answer: This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

77 661

Repost from Machine Learning & Artificial Intelligence | Data Science Free Courses

🔰 Python for Machine Learning & Data Science Masterclass ⏱ 44 Hours 📦 170 Lessons Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more! Taught By: Jose Portilla Download Full Course: https://t.me/datasciencefree/69 Download All Courses: https://t.me/datasciencefree/2

77 661

Jeroen_Janssens_Data_Science_at_the_Command_Line_Obtain,_Scrub,.epub7.96 MB

77 661

Core machine learning concepts explained through memes and simple charts created by Mihail Eric.

77 661

Natural_Language_Processing_with_Python_by_Steven_Bird,_Ewan_Klein.pdf5.18 MB

77 661

Machine-Learning-With-Python-For-Everyone-Pearson-2020.pdf9.00 MB

77 661

DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS 1. What is a logistic function? What is the range of values of a logistic function? f(z) = 1/(1+e -z ) The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity. 2. What is the difference between R square and adjusted R square? R square and adjusted R square values are used for model validation in case of linear regression. R square indicates the variation of all the independent variables on the dependent variable. i.e. it considers all the independent variable to explain the variation. In the case of Adjusted R squared, it considers only significant variables(P values less than 0.05) to indicate the percentage of variation in the model. Thus Adjusted R2 is always lesser then R2. 3. What is stratify in Train_test_split? Stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset. So if my input data has 60% 0's and 40% 1's as my class label, then my train and test dataset will also have the similar proportions. 4. What is Backpropagation in Artificial Neuron Network? Backpropagation is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization. ENJOY LEARNING 👍👍

77 661

https://t.me/CryptoSignalsLeaked/7791 We are giving away 100$ in cash, two memberships for R&A signals for 1 month, and it's allll for free!🔥 All you Don't miss out! It's free for everyone!

77 661

Thinking_in_Pandas_How_to_Use_the_Python_Data_Analysis_Library_the.pdf2.31 MB

77 661

How is kNN different from k-means clustering? kNN, or k-nearest neighbors is a classification algorithm, where the k is an integer describing the number of neighboring data points that influence the classification of a given observation. K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data. Both accomplish different tasks.