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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
Show more๐ 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 75 822 subscribers, ranking 2 109 in the Education category and 4 254 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 75 822 subscribers.
According to the latest data from 20 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 833 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.15%. Within the first 24 hours after publication, content typically collects 1.15% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 391 views. Within the first day, a publication typically gains 875 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.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataโ
Thanks to the high frequency of updates (latest data received on 21 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.
75 822
Subscribers
+124 hours
+1047 days
+83330 days
Posts Archive
Fundamentals and Methods of Machine and Deep Learning
Pradeep Singh, 2022
1.What is the meaning of term weight initialization in neural networks?
In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small.
2.What is Cross-validation in Machine Learning?
Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
โข Holdout method
โข K-fold cross-validation
โข Stratified k-fold cross-validation
โข Leave p-out cross-validation
3.What is a Self-Join?
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
4. What are the types of views in SQL?
In SQL, the views are classified into four types. They are:
Simple View: A view that is based on a single table and does not have a GROUP BY clause or other features.
Complex View: A view that is built from several tables and includes a GROUP BY clause as well as functions.
Inline View: A view that is built on a subquery in the FROM clause, which provides a temporary table and simplifies a complicated query.
Materialized View: A view that saves both the definition and the details. It builds data replicas by physically preserving them.
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Designing User Interfaces With a Data Science Approach
Abhijit Narayanrao, 2022
You don't need to spend several $๐ญ๐ฌ๐ฌ๐ฌ๐ to learn Data Science.โ
Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.๐ฅ
Here's 8 free Courses that'll teach you better than the paid ones:
1. CS50โs Introduction to Artificial Intelligence with Python (Harvard)
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2. Data Science: Machine Learning (Harvard)
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3. Artificial Intelligence (MIT)
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4. Introduction to Computational Thinking and Data Science (MIT)
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5. Machine Learning (MIT)
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6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)
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7. Statistical Learning (Stanford)
https://lnkd.in/dymn4hbD
8. Mining Massive Data Sets (Stanford)
๐https://lnkd.in/d2uf-FkB
1. What is Dimensionality Reduction?
In the real world, Machine Learning models are built on top of features and parameters. These features can be multidimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them. This is where dimensionality reduction is used to cut down irrelevant and redundant features with the help of principal variables. These principal variables conserve the features, and are a subgroup, of the parent variables.
2.What is the bin in tableau?
Bins in tableau are containers of equal size used to store data values fitting in bin size. In other words, bins group the data into groups of equal size or data which can be used in systematic viewing of data. All the discrete fields in tableau can also be considered as set of bins.
3.Whatโs a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, itโs how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domainโitโs a very common way to extract features from audio signals or other time series such as sensor data.
4. What are Superkey and candidate key in SQL?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attribute to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
You don't need to buy a GPU for machine learning work!
There are other alternatives. Here are some:
1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
Spend your time focusing on your problem.๐ช๐ช Let others worry about the hardware!!
Python Machine Learning Projects
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Some of the essential libraries of Python that are used in Data Science
Numpy
SciPy
Pandas
Matplotlib
Keras
TensorFlow
Scikit-learn
tshilidzi-marwala-handbook-of-machine-learning-volume.pdf14.38 MB
Important Topics to become a data scientist
[Advanced Level]
๐๐
1. Mathematics
Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification
2. Probability
Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution
3. Statistics
Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression
4. Programming
Python:
Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn
R Programming:
R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny
DataBase:
SQL
MongoDB
Data Structures
Web scraping
Linux
Git
5. Machine Learning
How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage
6. Deep Learning
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification
7. Feature Engineering
Baseline Model
Categorical Encodings
Feature Generation
Feature Selection
8. Natural Language Processing
Text Classification
Word Vectors
9. Data Visualization Tools
BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense
10. Deployment
Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django
ENJOY LEARNING ๐๐
