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 77 647 suscriptores, ocupando la posición 2 015 en la categoría Educación y el puesto 3 825 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 77 647 suscriptores.
Según los últimos datos del 12 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -123, 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 2.52%. 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 1 954 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 13 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.
77 647
Suscriptores
-2124 horas
-767 días
-12330 días
Archivo de publicaciones
😉5 Machine Learning Algorithms with Project Ideas
📉Linear Regression -> House Price Prediction
📈Logistic Regression -> Loan Default Prediction
🗞️ SVM -> News Classification
🏛️ KNN -> Breast Cancer Classification
🧮 Naive Bayes -> Text Classification
Data Science Bookcamp Five real-world Python projects.pdf42.41 MB
Decision trees and Random forests?
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables.
Random Forest is a versatile machine learning method capable of performing both regression and classification tasks. It also undertakes dimensional reduction methods, treats missing values, outlier values and other essential steps of data exploration, and does a fairly good job. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.
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Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
Pandas Tricks to Create a DataFrame From an Existing One.pdf5.32 KB
Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf11.81 MB
Ultimate Python Bootcamp For Data Science & Machine Learning
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https://t.me/udemy_free_courses_with_certi/1027
[Free for limited time only]
How Uber works
With a huge database of drivers, as soon as a user requests for car, their algorithms match a user with the most suitable driver within a 15 second window to the nearest driver. Uber stores and analyses data on every single trip the users take which is leveraged to predict the demand for cars, set the fares and allocate sufficient resources. Data science team at Uber also performs in-depth analysis of the public transport networks across different cities so that they can focus on cities that have poor transportation and make the best use of the data to enhance customer service experience.
Ankur_A_Patel_Hands_On_Unsupervised_Learning_Using_Python_How_to.epub4.59 MB
DATA SCIENCE INTERVIEW QUESTIONS
[PART -19]
Q. What is a bias-variance trade-off?
A. If the algorithm is too basic (hypothesis with linear eq. ), it may be prone to errors due to strong bias and low variance. If the algorithms are too sophisticated (hypothesis with a high degree eq. ), the variance and bias may be considerable. The new entries will not fare well in the latter scenario. Trade-off, also known as Bias Variance Trade-off, is something that exists between these two situations. There is a tradeoff between bias and variance because of this tradeoff in complexity. It's impossible for an algorithm to be both more complex and less complex at the same time.
Q. What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. Describe different regularization methods, such as L1 and L2 regularization?
A. L1 regularization, also known as L1 norm or Lasso (in regression problems), combats overfitting by shrinking the parameters towards 0. This makes some features obsolete. L2 regularization, or the L2 norm, or Ridge (in regression problems), combats overfitting by forcing weights to be small, but not making them exactly 0.
Q. What is correlation and covariance in statistics?
A. Both covariance and correlation measure the relationship and the dependency between two variables. Covariance indicates the direction of the linear relationship between variables. Correlation measures both the strength and direction of the linear relationship between two variables. Correlation values are standardized. Covariance values are not standardized.
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Machine Learning with Python for Everyone
👇👇
https://mariapilot.noblogs.org/files/2020/10/Machine-Learning-With-Python-For-Everyone-Pearson-2020.pdf
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