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Codehub
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📈 Análisis del canal de Telegram Codehub
El canal Codehub (@pythonadvisorai) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 33 758 suscriptores, ocupando la posición 4 063 en la categoría Tecnologías y Aplicaciones y el puesto 1 015 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 33 758 suscriptores.
Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -497, y en las últimas 24 horas de -21, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.13%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 734 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Free Programming resources.”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 07 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
33 758
Suscriptores
-2124 horas
-1097 días
-49730 días
Archivo de publicaciones
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/
