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Data Science & Machine Learning

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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

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📈 Análisis del canal de Telegram Data Science & Machine Learning

El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 77 621 suscriptores, ocupando la posición 2 017 en la categoría Educación y el puesto 3 821 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 77 621 suscriptores.

Según los últimos datos del 13 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -142, y en las últimas 24 horas de -20, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.37%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.01% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 843 visualizaciones. En el primer día suele acumular 781 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 14 noviembre, 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 Educación.

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77 621

Suscriptores

-2024 horas

-957 días

-14230 días

1 843

Visitas de la publicación

~ 78124 horas

~ 1 05048 horas

2.37%

Tasa de compromiso

~ 2

Mensajes por día

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Archivo de publicaciones

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Quiz Explaination Supervised Learning: All data is labeled and the algorithms learn to predict the output from the input data Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used to solve problem. Unsupervised learning problems can be further grouped into clustering and association problems. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy A also tend to buy B.

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Which of the following is not a type of unsupervised Learning?

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In which technique, data is unlabeled and the algorithms learn to inherent structure from the input data?

Anonymous voting

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Master_Machine_Learning_Algorithms_Discover_how_they_work_by_Jason.pdf1.09 MB

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machine-learning-interview-questions.pdf2.11 KB

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What are the main parameters of the random forest model? max_depth: Longest Path between root node and the leaf min_sample_split: The minimum number of observations needed to split a given node max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees min_samples_leaf: minimum number of samples in the leaf node n_estimators: Number of trees max_sample: Fraction of original dataset given to any individual tree in the given model max_features: Limits the maximum number of features provided to trees in random forest model

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🎓 Introduction to Deep Learning (by MIT) 🎓 This is one of the top high-quality courses to learn the foundational knowledge of deep learning. All lectures have been uploaded. 100% Free! https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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Pandas in 8 Pages.pdf8.28 KB

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What are the main parameters in the gradient boosting model? There are many parameters, but below are a few key defaults. learning_rate=0.1 (shrinkage). n_estimators=100 (number of trees). max_depth=3. min_samples_split=2. min_samples_leaf=1. subsample=1.0.

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Which of the following Python Library can be exclusively used to plot graphs?

Anonymous voting

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How does L2 regularization look like in a linear model? L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter. This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.

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Which regularization techniques do you know? There are mainly two types of regularization, L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function. L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function Here, Lambda determines the amount of regularization.

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Machine Learning for Everyone in some words https://vas3k.com/blog/machine_learning/

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Everything you need to know about TensorFlow 2.0 Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more. https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074?

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Today is the last day to get exclusive 75% discount by using coupon code JULY75

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What happens to our linear regression model if we have three columns in our data: x, y, z — and z is a sum of x and y? We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.

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What’s the difference between random forest and gradient boosting? Random Forests builds each tree independently while Gradient Boosting builds one tree at a time. Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.

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Free Live session on Deep Learning technology used by Amazon Go 👇👇 https://bit.ly/3e1BCLs Only for Indian Users

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Which algorithm builds one tree at a time?

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What do we do with categorical variables? Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including: One-hot encoding Label encoding Ordinal encoding Target encoding