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Coding & AI Resources

Coding & AI Resources

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๐Ÿ“šGet daily updates for : โœ… Free resources โœ… All Free notes โœ… Internship,Jobs and a lot more....๐Ÿ˜ ๐Ÿ“Join & Share this channel with your friends and college mates โค๏ธ Managed by: @love_data Buy ads: https://telega.io/c/leadcoding

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๐Ÿ“ˆ Analytical overview of Telegram channel Coding & AI Resources

Channel Coding & AI Resources (@leadcoding) in the English language segment is an active participant. Currently, the community unites 35 479 subscribers, ranking 5 363 in the Education category and 11 803 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 35 479 subscribers.

According to the latest data from 12 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 74 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.68%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 307 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
  • Thematic interests: Content is focused on key topics such as learning, link:-, element, programming, analytic.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œ๐Ÿ“šGet daily updates for : โœ… Free resources โœ… All Free notes โœ… Internship,Jobs and a lot more....๐Ÿ˜ ๐Ÿ“Join & Share this channel with your friends and college mates โค๏ธ Managed by: @love_data Buy ads: https://telega.io/c/leadcodingโ€

Thanks to the high frequency of updates (latest data received on 13 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.

35 479
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+124 hours
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Posts Archive
Repost from Generative AI
๐Ÿฑ ๐—•๐—ฒ๐˜€๐˜ ๐—œ๐—•๐—  ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜ 1)Python for Data Science 2)SQL & Relational Databas
๐Ÿฑ ๐—•๐—ฒ๐˜€๐˜ ๐—œ๐—•๐—  ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜  1)Python for Data Science  2)SQL & Relational Databases  3)Applied Data Science with Python  4)Machine Learning with Python  5)Data Analysis with Python ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:-  https://pdlink.in/3QyJyqk Enroll For FREE & Get Certified๐ŸŽ“

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Hands-On Data Science and Python Machine Learning Frank Kane, 2017

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๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜ Master AI for FREE: 5 Must-Take Google Courses to Boost Your Career ๐ŸŒŸ Artificial Intelligence is transforming industries, and nowโ€™s your chance to dive into this exciting field with free, expert-led courses by Google. ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/428e55o Enroll Now & Get Certfied ๐ŸŽ“

Expert Python Programming (2021) 100 likes = new books

๐ŸŒป ๐—จ๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ ๐—•๐—ถ๐—ด ๐—ข ๐—ป๐—ผ๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป! O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position. O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half. O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one. O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort. O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list. O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach. O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.

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learning-nodejs-development(5).pdf50.54 MB

๐—•๐—ฒ๐˜€๐˜ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ๐˜€ ๐—ง๐—ผ ๐—•๐—ผ๐—ผ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ๐Ÿ˜ 1๏ธโƒฃ BCG Data Science & Analyt
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Common Coding Mistakes to Avoid Even experienced programmers make mistakes.
Undefined variables:
Ensure all variables are declared and initialized before use.
Type coercion:
Be mindful of JavaScript's automatic type conversion, which can lead to unexpected results.
Incorrect scope:
Understand the difference between global and local scope to avoid unintended variable access.
Logical errors:
Carefully review your code for logical inconsistencies that might lead to incorrect output.
Off-by-one errors:
Pay attention to array indices and loop conditions to prevent errors in indexing and iteration.
Infinite loops:
Avoid creating loops that never terminate due to incorrect conditions or missing exit points. Example: // Undefined variable error let result = x + 5; // Assuming x is not declared // Type coercion error let age = "30"; let isAdult = age >= 18; // Age will be converted to a number By being aware of these common pitfalls, you can write more robust and error-free code. Do you have any specific coding mistakes you've encountered recently? #javascript #codingtips #errors #bestpractices

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So accurate
So accurate

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