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Machine Learning & Artificial Intelligence | Data Science Free Courses

Machine Learning & Artificial Intelligence | Data Science Free Courses

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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun

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๐Ÿ“ˆ Analytical overview of Telegram channel Machine Learning & Artificial Intelligence | Data Science Free Courses

Channel Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) in the English language segment is an active participant. Currently, the community unites 66 652 subscribers, ranking 2 465 in the Education category and 432 in the Malaysia region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 66 652 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 571 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 0.92%. Within the first 24 hours after publication, content typically collects 0.79% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 612 views. Within the first day, a publication typically gains 524 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as sellerflash, waybienad, pricing, buybox, buyer.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œPerfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfunโ€

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.

66 652
Subscribers
+224 hours
+417 days
+57130 days
Posts Archive
๐—ง๐—ผ๐—ฝ ๐Ÿฑ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ฌ๐—ผ๐˜‚ ๐—–๐—ฎ๐—ป ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—œ๐—ป ๐—ง๐—ผ๐—ฑ๐—ฎ๐˜†!๐Ÿ˜ In todayโ€™s fast-paced tech
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๐Ÿ”— Become a Machine Learning Expert in 7 Steps
๐Ÿ”— Become a Machine Learning Expert in 7 Steps

โŒจ๏ธ Python Tips & Tricks
+3
โŒจ๏ธ Python Tips & Tricks

๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฃ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ถ๐˜€ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ข๐—ฟ๐—ฎ๐—ฐ๐—น๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฃ๏ฟฝ
๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฃ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ถ๐˜€ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ข๐—ฟ๐—ฎ๐—ฐ๐—น๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฃ๐—ฎ๐˜๐—ต!๐Ÿ˜ Want to start a career in Data Science but donโ€™t know where to begin?๐Ÿ‘‹ Oracle is offering a ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฃ๐—ฎ๐˜๐—ต to help you master the essential skills needed to become a ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฃ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐—ฎ๐—น๐Ÿ“Š ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3Dka1ow Start your journey today and become a certified Data Science Professional!โœ…๏ธ

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Preparing for a machine learning interview as a data analyst is a great step. Here are some common machine learning interview questions :- 1. Explain the steps involved in a machine learning project lifecycle. 2. What is the difference between supervised and unsupervised learning? Give examples of each. 3. What evaluation metrics would you use to assess the performance of a regression model? 4. What is overfitting and how can you prevent it? 5. Describe the bias-variance tradeoff. 6. What is cross-validation, and why is it important in machine learning? 7. What are some feature selection techniques you are familiar with? 8.What are the assumptions of linear regression? 9. How does regularization help in linear models? 10. Explain the difference between classification and regression. 11. What are some common algorithms used for dimensionality reduction? 12. Describe how a decision tree works. 13. What are ensemble methods, and why are they useful? 14. How do you handle missing or corrupted data in a dataset? 15. What are the different kernels used in Support Vector Machines (SVM)? These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role. Good luck with your interview preparation! Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Like if you need similar content ๐Ÿ˜„๐Ÿ‘

Data Scientist vs Data Analyst ๐Ÿ‘†
Data Scientist vs Data Analyst ๐Ÿ‘†

๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—•๐—ผ๐—ผ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ!๐Ÿ˜ Want to upgrade your tech & data skills withou
๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—•๐—ผ๐—ผ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ!๐Ÿ˜ Want to upgrade your tech & data skills without spending a penny?๐Ÿ”ฅ These ๐—™๐—ฅ๐—˜๐—˜ courses will help you master ๐—˜๐˜…๐—ฐ๐—ฒ๐—น, ๐—”๐—œ, ๐—– ๐—ฝ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ถ๐—ป๐—ด, & ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป Interview Prep!๐Ÿ“Š ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4ividkN Start learning today & take your career to the next level!โœ…๏ธ

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Machine learning algorithms
Machine learning algorithms

๐—™๐—ฟ๐—ฒ๐—ฒ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜„๐—ถ๐˜๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ๐˜€!๐Ÿ˜ Want to boost your skills with industry-recog
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An high level overview for becoming a machine learning engineer
An high level overview for becoming a machine learning engineer