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Data Science & Machine Learning

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

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📈 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 75 899 subscribers, ranking 2 103 in the Education category and 4 204 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 899 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 731 over the last 30 days and by 33 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.95%. Within the first 24 hours after publication, content typically collects 0.86% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 239 views. Within the first day, a publication typically gains 650 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.

📝 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 For collaborations: @love_data

Thanks to the high frequency of updates (latest data received on 24 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.

75 899
Subscribers
+3324 hours
+587 days
+73130 days
Posts Archive
Hands-On Unsupervised Learning Using Python.pdf5.63 MB

Python for Data Science Course @datasciencefun

Machine learning using Python

Top 50 Machine Learning Interview Q&A.pdf2.61 KB

Lynda.com - Data Science Foundations - Fundamentals.zip665.81 MB

Free ML webinar to learn how Swiggy uses Data Science! Link: https://bit.ly/3gNRBy0 ✅ Only for Indian users
Free ML webinar to learn how Swiggy uses Data Science! Link: https://bit.ly/3gNRBy0 ✅ Only for Indian users

Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words? Usually logistic regression is better because bag of words creates a matrix with large number of columns. For a huge number of columns logistic regression is usually faster than gradient boosting trees.

MathforML.pdf5.00 MB

Do you have a business idea? We are waiting for you! We are the first international channel PitchCamp, which prepared for you
Do you have a business idea? We are waiting for you! We are the first international channel PitchCamp, which prepared for you: ✔️Weekly opportunity to win $500 - $5000 just for business idea ✔️Daily educational tools & analytics ✔️Daily advises from our experts ✔️Live Performance from our active startups ✔️Up to $150.000 from our community for real start up Make the first step, get information and apply for weekly contest. 👉 PitchCamp

Python Programming. Python Programming for Beginners, Python Programming for Intermediates

Artificial Neural Networks with Java

LinkedIn - Python for Data Science Essential Training Part 2.zip390.08 MB

🌐 Join the researchers and programmers channel (Courses, Books, Papere and Codes). t.me/DataScience_Books

Advanced ML with Python

Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications - Laura Igual, Santi Segui (Springer, 2017)

What is unsupervised learning? Unsupervised learning aims to detect patterns in the data where no labels are given.

Tableau_Cheatsheet.pdf1.65 KB

git-cheat-sheet-education.pdf0.98 KB

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.

How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bi
How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bit.ly/2SLpFlw