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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 75 833 subscribers, ranking 2 106 in the Education category and 4 234 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 833 subscribers.

According to the latest data from 21 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 770 over the last 30 days and by 8 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 1.09% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 385 views. Within the first day, a publication typically gains 827 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 22 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.

75 833
Subscribers
+824 hours
+717 days
+77030 days
Posts Archive
The Pandas Workshop.pdf28.94 MB

Introduction to Machine Learning with Applications in Information Security Mark Stamp, 2022

Machine Learning Projects 👇👇 https://t.me/Programming_experts/133

ML Cheatsheet.pdf1.25 MB

Azure Data Scientist Associate Certification Guide Andreas Botsikas, 2021

Super VIP cheat sheet for Data Scientists.pdf7.12 MB

Statistical Mechanics of Neural Networks Haiping Huang, 2021

Data Analyst Interview Questions [Python, SQL, PowerBI] 1. Is indentation required in python? Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well. 2. What are Entities and Relationships? Ans: Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities. Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database. 3. What are Aggregate and Scalar functions? Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value. 4. What are Custom Visuals in Power BI? Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI ENJOY LEARNING 👍👍

Efficient Methods for DL.pdf9.72 MB

Useful Pandas🐼 method you should definitely know ✅ head() ✅ info() ✅ fillna() ✅ melt() ✅ pivot() ✅ query() ✅ merge() ✅ assign() ✅ groupby() ✅ describe() ✅ sample() ✅ replace() ✅ rename()

To become a Machine Learning Engineer: • Python • numpy, pandas, matplotlib, Scikit-Learn • TensorFlow or PyTorch • Jupyter, Colab • Analysis > Code • 99%: Foundational algorithms • 1%: Other algorithms • Solve problems ← This is key • Teaching = 2 × Learning • Have fun!

BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

Natural Language Processing with TensorFlow Thushan Ganegedara, 2022

BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

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Linear Algebra and learning from data Gilbert Strang, 2019

BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

Basics of Linear Algebra for Machine Learning Jason Brownlee, 2018

Practical Linear Algebra For Data Science Mike X. Cohen, 2022

Think Stats Allen B. Downey, 2011