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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

Network: Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertIndia3 822Education2 014...

📈 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 77 677 subscribers, ranking 2 014 in the Education category and 3 822 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 677 subscribers.

According to the latest data from 11 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -97 over the last 30 days and by -12 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.59%. Within the first 24 hours after publication, content typically collects 0.99% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 009 views. Within the first day, a publication typically gains 767 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 12 November, 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.

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77 677

Subscribers

-1224 hours

-647 days

-9730 days

2 009

Post views

~ 76724 hours

~ 1 03648 hours

2.59%

Engagement rate

~ 1

Posts per day

Ads index

Posts Archive

77 661

Artificial Intelligence for Games.pdf17.63 MB

77 661

Mastering Machine Learning for Penetration Testing.epub8.95 MB

77 661

Data Analysis Interterview Questions and Answers 👇👇 1.How to create filters in Power BI? Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways. Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries. Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters) 2.How to sort data in Power BI? Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available. 3.How to convert pdf to excel? Open the PDF document you want to convert in XLSX format in Acrobat DC. Go to the right pane and click on the “Export PDF” option. Choose spreadsheet as the Export format. Select “Microsoft Excel Workbook.” Now click “Export.” Download the converted file or share it. 4. How to enable macros in excel? Click the file tab and then click “Options.” A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.” Go to the “Macro Settings” and select “enable all macros.” Click OK to apply the macro settings. ————————————————————- ENJOY LEARNING 👍👍

77 661

Machine Learning Fundamentals: A Concise Introduction PDF

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Which of the following tool can be used for data visualization?

Anonymous voting

77 661

Python for data science 2022.pdf2.42 MB

77 661

Artificial Intelligence in Games PDF

77 661

Python For Data Science 2022 PDF

77 661

Fundamentals_of_Deep_Learning,_2nd_Edition_Fourth_Early_Release.pdf10.94 MB

77 661

Deep Learning Masterpiece .pdf14.46 MB

77 661

Distributed Machine Learning with Python PDF

77 661

Introduction to R Programming👉

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New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc. Don't get bogged down trying to learn every new term & technology you come across. Instead, focus on foundations. - data wrangling - visualizing - exploring - modeling - understanding the results. The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!

77 661

The Machine Learning Solutions Architect Handbook PDF

77 661

Data Engineering with Google Cloud Platform PDF

77 661

Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-to-end PDF

77 661

Numpy Handbook.pdf3.75 KB

77 661

Probability Cheat Sheet 👇👇 Cheat Sheet

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💥Deep Learning with Pytorch by Prof.Yann LeCun (CNN Founder) This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. GitHub Link: https://atcold.github.io/pytorch-Deep-Learning/ YouTube Playlist: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq