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 31 526 suscriptores, ocupando la posición 4 219 en la categoría Tecnologías y Aplicaciones y el puesto 1 037 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 31 526 suscriptores.
Según los últimos datos del 28 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -428, y en las últimas 24 horas de -8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.54%. 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 0 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 0.
📝 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 29 octubre, 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.
31 526
Suscriptores
-824 horas
-927 días
-42830 días
Archivo de publicaciones
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FRONT_END DEVELOPER ROADMAP - https://inprogrammer.com/web-stories/front-end-developer-roadmap/
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Algorithm Name
PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
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Algorithm Name
Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
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Algorithm Name
Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
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Algorithm Name
Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
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Algorithm Name
K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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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
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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
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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
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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
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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
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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
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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
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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
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Algorithm
Linear regression
Description
Finds a way to correlate each feature to the output to help predict future values.
Type
Regression
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learn build your own game using python🥰👨💻 - https://inprogrammer.com/web-stories/learn-build-your-own-game-using-python/
