Available now! Telegram Research 2025 β the year's key insights 
Data Science & Machine Learning
Open in 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
Show moreπ Analytical overview of Telegram channel Data Science & Machine Learning
Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 860 subscribers, ranking 2 107 in the Education category and 4 219 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 75 860 subscribers.
According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 728 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.00%. Within the first 24 hours after publication, content typically collects 1.05% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 278 views. Within the first day, a publication typically gains 794 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataβ
Thanks to the high frequency of updates (latest data received on 23 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
75 860
Subscribers
-224 hours
+637 days
+72830 days
Posts Archive
Learn how Amazon Optimise Delivery Routes using Genetic Algorithm
ππ
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
π€ 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
π Introduction to Deep Learning (by MIT) π
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?
How does L2 regularization look like in a linear model?
L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.
This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.
Which regularization techniques do you know?
There are mainly two types of regularization,
L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function
Here, Lambda determines the amount of regularization.
