Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

Red:Programming, data science, ML - free courses by Big Data Specialist India29 106 Tecnologías y Aplicaciones8 932...

📈 Análisis del canal de Telegram Data science/ML/AI

El canal Data science/ML/AI (@datascience_bds) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 13 899 suscriptores, ocupando la posición 8 932 en la categoría Tecnologías y Aplicaciones y el puesto 29 106 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 899 suscriptores.

Según los últimos datos del 27 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 81, 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 8.01%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.06% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 113 visualizaciones. En el primer día suele acumular 287 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como panda, learning, row, api, ethic.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 28 agosto, 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.

13 899

Suscriptores

+124 horas

-97 días

+8130 días

1 113

Visitas de la publicación

~ 28724 horas

~ 41048 horas

8.01%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

13 899

A Stanford CS' Lecture note diving into supervised/unsupervised algorithms, neural networks, SVMs with math proofs and Python pseudocode.

13 899

📚 Data Science Riddle A numeric feature has many repeated exact values with occasional jumps. What type of variable is this?

Anonymous voting

13 899

AI vs Machine Learning vs Deep Learning Vs Generative AI

13 899

4 Pillars of Data Science

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📚 Data Science Riddle You fit a forecasting model and residuals show increasing variance. What is needed?

Anonymous voting

13 899

Repost from Programming, data science, ML - free courses by Big Data Specialist

Notes on HDFS, MapReduce, YARN, Hadoop vs. traditional systems and much more... from Columbia University.

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📚 Data Science Riddle Your spark job fails due to executor memory pressure. Most effective optimization?

Anonymous voting

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📚 Data Science Riddle You're working with highly noisy user text. Which tokenization met6handles misspellings best?

Anonymous voting

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Eigenvalues & Eigenvectors — Why PCA Actually Works You’ve heard of PCA. But what’s really happening underneath? PCA finds the directions (vectors) where your data varies the most. Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures. You’re basically rotating your data to find its “natural axes.”

PCA isn’t compression — it’s discovering how your data wants to be seen.

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📚 Data Science Riddle You're Processing a dataset with frequent schema evolution. Which format handles it most gracefully?

Anonymous voting

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Covariance vs. Correlation: Same Family, Different Story People use them interchangeably but they measure different things. Covariance tells you the direction of relationship (positive or negative). Correlation goes further; it tells you the strength, normalized between -1 and 1. So while covariance can be 2345.67, correlation says 0.92. clear, interpretable, scale-free.

Covariance shows movement, correlation shows consistency.

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K-Means Clustering

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📚 Data Science Riddle A data engineer complains that your model training job is failing in production due to schema mismatch. What's the root fix?

Anonymous voting

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Top 6 Data Concepts

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📚 Data Science Riddle In a real-world NLP project, your model performs poorly on new slang abbreviations. What's the fix?

Anonymous voting

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Covers basics of Linear Regression for modeling numerical data, including assumptions and applications in genetics, from University of Washington.

13 899

Regression Analysis Cheatsheet

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Repost from Programming, data science, ML - free courses by Big Data Specialist

This is our latest post from Instagram, saved as PDF. It's a comprehensive breakdown(as always) explaining the difference between Relational DB and Graph DB in a fun and easy to grasp way. ⚠️ Spoiler alert: You will love it! Here's our Instagram post: Relational DB Vs Graph DB

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Covers basic numerical and graphical summaries with practical examples, from University of Washington.

13 899

Repost from Cool GitHub repositories

lerobot This is an end-to-end library for robot learning. It handles the entire pipeline from loading and processing robotics datasets to training policies and deploying them in simulation or on real hardware. Creator: huggingface Stars ⭐️: 19,000 Forked by: 3,000 Github Repo: https://github.com/huggingface/lerobot #robotics #AI ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group