Data Science & Machine Learning Resources
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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 Admin: @love_data Buy ads: https://telega.io/c/datalemur
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Network:📈 Analytical overview of Telegram channel Data Science & Machine Learning Resources
Channel Data Science & Machine Learning Resources (@datalemur) in the English language segment is an active participant. Currently, the community unites 20 597 subscribers, ranking 9 687 in the Education category and 20 482 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 20 597 subscribers.
According to the latest data from 30 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 127 over the last 30 days and by 6 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.66%. Within the first 24 hours after publication, content typically collects 0.68% reactions from the total number of subscribers.
- Post reach: On average, each post receives 547 views. Within the first day, a publication typically gains 140 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as |--, learning, insidead, database, sql.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
Admin: @love_data
Buy ads: https://telega.io/c/datalemur”
Thanks to the high frequency of updates (latest data received on 31 July, 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.
20 597
Subscribers
+624 hours
+447 days
+12730 days
Data loading in progress...
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| Date | Subscriber Growth | Mentions | Channels | |
| 31 July | +8 | |||
| 30 July | +6 | |||
| 29 July | +14 | |||
| 28 July | +9 | |||
| 27 July | +10 | |||
| 26 July | +1 | |||
| 25 July | +12 | |||
| 24 July | +10 | |||
| 23 July | +8 | |||
| 22 July | +12 | |||
| 21 July | +14 | |||
| 20 July | +16 | |||
| 19 July | +12 | |||
| 18 July | +7 | |||
| 17 July | +8 | |||
| 16 July | +5 | |||
| 15 July | +11 | |||
| 14 July | +2 | |||
| 13 July | +8 | |||
| 12 July | +2 | |||
| 11 July | +5 | |||
| 10 July | +1 | |||
| 09 July | +5 | |||
| 08 July | +9 | |||
| 07 July | +4 | |||
| 06 July | +12 | |||
| 05 July | +7 | |||
| 04 July | +18 | |||
| 03 July | 0 | |||
| 02 July | +3 | |||
| 01 July | 0 |
Channel Posts
Data Science Roadmap: 🗺
📂 Math & Stats
∟📂 Python/R
∟📂 Data Wrangling
∟📂 Visualization
∟📂 ML
∟📂 DL & NLP
∟📂 Projects
∟ ✅ Apply For Job
Like if you need detailed explanation step-by-step ❤️
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| 3 | Top 10 machine Learning algorithms 👇👇
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
Credits: https://t.me/datasciencefun
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| 5 | 🎯Free AI/ML learning resources 👇
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| 7 | SQL vs Python Programming: Quick Comparison ✍
📌 SQL Programming
• Query data from databases
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Best fields
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Job titles
• Data Analyst
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• BI Analyst
• SQL Developer
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• Clean and analyze data
• Automate workflows
• Build models
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• Notebooks
• Scripts
• ML pipelines
Best fields
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Job titles
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• Python Developer
Hiring reality
• Common in mid to senior roles
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India salary range
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Real tasks
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⚔️ Quick comparison
• Data source
SQL stays inside databases
Python pulls data from anywhere
• Speed
SQL runs fast on large tables
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