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📈 Аналитический обзор Telegram-канала Codehub
Канал Codehub (@pythonadvisorai) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 33 758 подписчиков, занимая 4 063 место в категории Технологии и приложения и 1 015 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 33 758 подписчиков.
Согласно последним данным от 05 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -497, а за последние 24 часа — -21, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 5.13%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 734 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Free Programming resources.”
Благодаря высокой частоте обновлений (последние данные получены 07 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
33 758
Подписчики
-2124 часа
-1097 дней
-49730 день
Архив постов
33 750
Unsupervised learning
In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
33 750
Algorithm
Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
33 750
Algorithm
AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
33 750
Algorithm
Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the ‘majority vote’ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
33 750
Algorithm
Support vector machine
Description
Support Vector Machine, or SVM, is typically used for the classification task.
SVM algorithm finds a hyperplane that optimally divided the classes. It is best used with a non-linear solver.
Type
Regression (not very common)
Classification
33 750
Algorithm
Naive Bayes
Description
The Bayesian method is a classification method that makes use of the Bayesian theorem. The theorem updates the prior knowledge of an event with the independent probability of each feature that can affect the event.
Type
Regression
Classification
33 750
Algorithm
Decision tree
Description
Highly interpretable classification or regression model that splits data-feature values into branches at decision nodes (e.g., if a feature is a color, each possible color becomes a new branch) until a final decision output is made
Type
Regression
Classification
33 750
Algorithm
Logistic regression
Description
Extension of linear regression that’s used for classification tasks. The output variable 3is binary (e.g., only black or white) rather than continuous (e.g., an infinite list of potential colors)
Type
Classification
33 750
Algorithm
Linear regression
Description
Finds a way to correlate each feature to the output to help predict future values.
Type
Regression
33 750
MOST IMPORTANT LINUX COMMANDS - https://inprogrammer.com/web-stories/most-important-linux-commands/
33 750
👆DevBytes is just the right app for professional and enthusiast programmers to stay in touch with all the latest updates, tips, tricks and jobs. It gives all programming news in less than 64 words and also has sharable code snippets for your reference.
App link :https://bit.ly/3SYjcNW
Download now !! 🔥
33 750
FREE PDF Download LINK🥵 https://inprogrammer.com/python-crash-course-free-pdf-download/
33 750
learn build your own game using python🥰👨💻 - https://inprogrammer.com/web-stories/learn-build-your-own-game-using-python/
33 750
Here are 27 ways to learn ethical hacking for free:
1. Root Me — Challenges.
2. Stök's YouTube — Videos.
3. Hacker101 Videos — Videos.
4. InsiderPhD YouTube — Videos.
5. EchoCTF — Interactive Learning.
6. Vuln Machines — Videos and Labs.
7. Try2Hack — Interactive Learning.
8. Pentester Land — Written Content.
9. Checkmarx — Interactive Learning.
10. Cybrary — Written Content and Labs.
11. RangeForce — Interactive Exercises.
12. Vuln Hub — Written Content and Labs.
13. TCM Security — Interactive Learning.
14. HackXpert — Written Content and Labs.
15. Try Hack Me — Written Content and Labs.
16. OverTheWire — Written Content and Labs.
17. Hack The Box — Written Content and Labs.
18. CyberSecLabs — Written Content and Labs.
19. Pentester Academy — Written Content and Labs.
20. Bug Bounty Reports Explained YouTube — Videos.
21. Web Security Academy — Written Content and Labs.
22. Securibee's Infosec Resources — Written Content.
23. Jhaddix Bug Bounty Repository — Written Content.
24. Zseano's Free Bug Bounty Methodology — Free Ebook.
25. Awesome AppSec GitHub Repository — Written Content.
26. NahamSec's Bug Bounty Beginner Repository — Written Content.
27. Kontra Application Security Training — Interactive Learning.
33 750
learn data science for python in 4 easy steps - https://inprogrammer.com/web-stories/learn-data-science-for-python-in-4-easy-steps/
