¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
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
Mostrar más📈 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 75 860 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 75 860 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.
75 860
Suscriptores
-224 horas
+637 días
+72830 días
Archivo de publicaciones
👨🎓 Best Telegram Channels👩🎓
💐 Free Courses with Certificate
👻Entertainment feelings sarcasm💀
🍃 Spring Boot Framework 🍃
⚖ Aesthetic Wisdom⚖
🎁Free Udemy Courses 🎁
📚 Learn Data Science📕
🎯 Jobs and Placement Guide
🧑💻Paid Courses for Free 🆓
🥇 Udacity Free Courses
🧿 Hack the Hacker 🧿
🧧 Register Your Channel
Learning and Practicing SQL: Resources and Platforms
1. https://sqlbolt.com/
2. https://sqlzoo.net/
3. https://www.codecademy.com/learn/learn-sql
4. https://www.w3schools.com/sql/
5. https://www.hackerrank.com/domains/sql
6. https://www.windowfunctions.com/
7. https://selectstarsql.com/
8. https://quip.com/2gwZArKuWk7W
9. https://leetcode.com/problemset/database/
10. http://thedatamonk.com/
Is accuracy always a good metric?
Accuracy is not a good performance metric when there is imbalance in the dataset. For example, in binary classification with 95% of A class and 5% of B class, a constant prediction of A class would have an accuracy of 95%. In case of imbalance dataset, we need to choose Precision, recall, or F1 Score depending on the problem we are trying to solve.
What are precision, recall, and F1-score?
Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).
Where TP is true positives, FP is false positives and FN is false negatives
In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.
F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.
Which website is best to learn Programming and Data science according to you?
👨🎓 Best Telegram Channels👩🎓
💐 Free Courses with Certificate
🥇 Free Programming Resources
🧿 Learn Hacking for Free
🎁Free Udemy Courses 🎁
📚 Learn Data Science📕
🍃 Spring Boot Framework 🍃
🎯 Jobs and Career Guide 🎯
👻Entertainment feelings sarcasm💀
🧑💻Paid Courses for Free 🆓
🧧 Promote Your Channel
How do we evaluate classification models?
Depending on the classification problem, we can use the following evaluation metrics:
Accuracy
Precision
Recall
F1 Score
Logistic loss (also known as Cross-entropy loss)
Jaccard similarity coefficient score
Highly recommended courses with real world projects to advance your career in Data Science
👉Become a Data scientists
https://bit.ly/2TRo9P7
👉Intermediate Python
https://bit.ly/3z7YPnZ
Use coupon code
SAVE75 for extra 75% off on above courses
Note: If you are a beginner in Python or Data science, then I will recommend you to enroll in the courses given in the pinned message instead of above courses
Offer valid till tomorrow onlyWhere to get data for your next machine learning project?
An overview of 5 amazing resources to accelerate your next project with data!
📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.
📌 Kaggle Dataset
Explore, analyze, and share quality data.
📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources
📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.
📌 Azure public data sets
Public data sets for testing and prototyping.
What is sigmoid? What does it do?
A sigmoid function is a type of activation function, and more specifically defined as a squashing function. Squashing functions limit the output to a range between 0 and 1, making these functions useful in the prediction of probabilities.
Sigmod(x) = 1/(1+e^{-x})
What is overfitting?
When your model perform very well on your training set but can't generalize the test set, because it adjusted a lot to the training set.
What is the bias-variance trade-off?
• Bias is the error introduced by approximating the true underlying function, which can be quite complex, by a simpler model. Variance is a model sensitivity to changes in the training dataset.
• Bias-variance trade-off is a relationship between the expected test error and the variance and the bias - both contribute to the level of the test error and ideally should be as small as possible:
ExpectedTestError = Variance + Bias² + IrreducibleError
• But as a model complexity increases, the bias decreases and the variance increases which leads to overfitting. And vice versa, model simplification helps to decrease the variance but it increases the bias which leads to underfitting.
Can you explain how cross-validation works?
Cross-validation is the process to separate your total training set into two subsets: training and validation set, and evaluate your model to choose the hyperparameters. But you do this process iteratively, selecting differents training and validation set, in order to reduce the bias that you would have by selecting only one validation set
What is K-fold cross-validation?
K fold cross validation is a method of cross validation where we select a hyperparameter k. The dataset is now divided into k parts. Now, we take the 1st part as validation set and remaining k-1 as training set. Then we take the 2nd part as validation set and remaining k-1 parts as training set. Like this, each part is used as validation set once and the remaining k-1 parts are taken together and used as training set. It should not be used in a time series data.
How to validate your models?
One of the most common approaches is splitting data into train, validation and test parts.
Models are trained on train data, hyperparameters (for example early stopping) are selected based on the validation data, the final measurement is done on test dataset.
Another approach is cross-validation: split dataset into K folds and each time train models on training folds and measure the performance on the validation folds.
Also you could combine these approaches: make a test/holdout dataset and do cross-validation on the rest of the data. The final quality is measured on test dataset.
Offer extended till 8 June
So use code
SAVE75 to get instant 75% discount in all the above courses before 8 JuneWhy is it require to split our data into three parts: train, validation, and test?
• The training set is used to fit the model, i.e. to train the model with the data.
• The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.
• Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.
Free Data Visualization course from EDX
https://online-learning.harvard.edu/course/data-science-visualization?delta=2
Free EDX course to learn linear Regression
https://online-learning.harvard.edu/course/data-science-linear-regression?delta=2
👨🎓 Best Telegram Channels👩🎓
💐 Free Courses with Certificate
🧿 Learn Hacking for Free
🎁Free Udemy Courses 🎁
📚 Data science Free Courses📕
🍃 Spring Boot Framework 🍃
🎯 Jobs and Internships 🎯
🥇Learn Programming and AI👨💻
👻Entertainment feelings sarcasm💀
🧑💻Paid Courses for Free 🆓
🧧 Promote Your Channel
Free Data wrangling course from EDX
👇👇
https://online-learning.harvard.edu/course/data-science-wrangling?delta=2
Free Course from EDX
👇👇
https://online-learning.harvard.edu/course/data-science-machine-learning?delta=2
Best Channel To Download All Programming Books And Courses
t.me/DataScience_Books
