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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
Mostrar más📈 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 75 822 suscriptores, ocupando la posición 2 109 en la categoría Educación y el puesto 4 254 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 822 suscriptores.
Según los últimos datos del 20 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 833, y en las últimas 24 horas de 1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.15%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.15% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 391 visualizaciones. En el primer día suele acumular 875 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 21 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 Educación.
75 822
Suscriptores
+124 horas
+1047 días
+83330 días
Archivo de publicaciones
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!
1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris
7. Titanic Dataset: https://www.kaggle.com/c/titanic
8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality
9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data
11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud
13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows
14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new
15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting
16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19
17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness
18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata
19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams
20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140
21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer
💻🔍 Don't miss out on these valuable resources for advancing your data science journey!
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Fundamentals of Machine Learning and Deep Learning in Medicine
Reza Borhani, 2022
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Fundamentals and Methods of Machine and Deep Learning
Pradeep Singh, 2022
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𝗔𝗻𝗱 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀, 𝘁𝗶𝗽𝘀 𝗮𝗻𝗱 𝘁𝗿𝗶𝗰𝗸𝘀.
💻 𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻 :- 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴, 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝗿𝗮𝗰𝗸𝗶𝗻𝗴, 𝗪𝗲𝗯 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗔𝗽𝗽 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗚𝗿𝗮𝗽𝗵𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗔𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻, 𝗩𝗶𝗱𝗲𝗼 𝗲𝗱𝗶𝘁𝗶𝗻𝗴, 𝗣𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆, 𝗣𝗵𝗼𝘁𝗼𝘀 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲 𝗹𝗼𝘁𝘀 𝗼𝗳 𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝗳𝗿𝗲𝗲 📚🏅🎖
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𝗚𝗲𝘁 𝗕𝘂𝗴 𝗕𝗼𝘂𝗻𝘁𝘆, 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴, 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗯𝗮𝘀𝗲𝗱 𝗲𝗕𝗼𝗼𝗸𝘀.
𝗜𝗻 𝘁𝗵𝗶𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹, 𝗬𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗨𝗱𝗲𝗺𝘆 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, & 𝗙𝗿𝗲𝗲𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀.
𝙁𝙤𝙧 𝙛𝙧𝙚𝙚 𝙘𝙤𝙪𝙧𝙨𝙚𝙨,𝙗𝙤𝙤𝙠𝙨,𝙥𝙧𝙤𝙟𝙚𝙘𝙩𝙨,𝙞𝙣𝙩𝙚𝙧𝙣𝙨𝙝𝙞𝙥𝙨,𝙥𝙡𝙖𝙘𝙚𝙢𝙚𝙣𝙩𝙨 𝙖𝙣𝙙 𝙟𝙤𝙗𝙨 𝙧𝙚𝙡𝙖𝙩𝙚𝙙 𝙢𝙖𝙩𝙚𝙧𝙞𝙖𝙡𝙨 𝙖𝙣𝙙 𝙪𝙥𝙙𝙖𝙩𝙚𝙨 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡:
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ARTIFICIAL_INTELLIGENCE_FOR_ROBOTICS @computer_books.pdf26.27 MB
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