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Codehub
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📈 Analytical overview of Telegram channel Codehub
Channel Codehub (@pythonadvisorai) in the English language segment is an active participant. Currently, the community unites 33 758 subscribers, ranking 4 063 in the Technologies & Applications category and 1 015 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 33 758 subscribers.
According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -497 over the last 30 days and by -21 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.13%. 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 1 734 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 3.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Free Programming resources.”
Thanks to the high frequency of updates (latest data received on 07 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 Technologies & Applications category.
33 758
Subscribers
-2124 hours
-1097 days
-49730 days
Posts Archive
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
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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/
