¡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 75 899 suscriptores, ocupando la posición 2 103 en la categoría Educación y el puesto 4 204 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 899 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 731, y en las últimas 24 horas de 33, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.95%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.86% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 239 visualizaciones. En el primer día suele acumular 650 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 24 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.
75 899
Suscriptores
+3324 horas
+587 días
+73130 días
Archivo de publicaciones
Machine Learning God by Stefan Stavrev (version 2.0).pdf1.57 MB
Some useful Machine Learning projects for practice
https://elitedatascience.com/machine-learning-projects-for-beginners
https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f
https://www.springboard.com/blog/machine-learning-projects
https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional
Learn how Amazon Optimise Delivery Routes using Genetic Algorithm
👇👇
https://bit.ly/3izxvZJ
Free Live Session with Certificate
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
🤓 Technical Python concepts tested in the data science job interviews are:
- Data types.
- Built-in data structures.
- User-defined data structures.
- Built-in functions.
- Loops and conditionals.
- External libraries (Pandas).
Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html
What are the benefits of a single decision tree compared to more complex models?
easy to implement
fast training
fast inference
good explainability
What are the decision trees?
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy.
What is feature selection? Why do we need it?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.
The session is going to go LIVE in 5 mins
So join in now !
Link - https://youtu.be/a8lGmSLcfWU
Do you want to Learn how to build a winning resume ?
Learn the basics from an expert in our mentor conference 2021 Resume session today!
Link to Join - https://youtu.be/a8lGmSLcfWU
If you are interested in getting a certificate and resources then kindly register at
www.foreignadmits.com/mentorconference2021
Note - Certificates will only be given to registered LIVE attendees
See you at the mentor conference
Join our community to get updates about other sessions -
https://t.me/foreignadmits_community
Quiz Explaination
Supervised Learning: All data is labeled and the algorithms learn to predict the output from the
input data
Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from
the input data.
Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of
supervised and unsupervised techniques can be used to solve problem.
Unsupervised learning problems can be further grouped into clustering and association problems.
Clustering: A clustering problem is where you want to discover the inherent groupings
in the data, such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy A also tend to buy B.
Which of the following is not a type of unsupervised Learning?
In which technique, data is unlabeled and the algorithms learn to inherent structure from the input data?
Master_Machine_Learning_Algorithms_Discover_how_they_work_by_Jason.pdf1.09 MB
What are the main parameters of the random forest model?
max_depth: Longest Path between root node and the leaf
min_sample_split: The minimum number of observations needed to split a given node
max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees
min_samples_leaf: minimum number of samples in the leaf node
n_estimators: Number of trees
max_sample: Fraction of original dataset given to any individual tree in the given model
max_features: Limits the maximum number of features provided to trees in random forest model
🎓 Introduction to Deep Learning (by MIT) 🎓
This is one of the top high-quality courses to learn the foundational knowledge of deep learning.
All lectures have been uploaded. 100% Free!
https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
What are the main parameters in the gradient boosting model?
There are many parameters, but below are a few key defaults.
learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.
Which of the following Python Library can be exclusively used to plot graphs?
