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 India31 693 Tecnologías y Aplicaciones9 381...

📈 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 13 667 suscriptores, ocupando la posición 9 381 en la categoría Tecnologías y Aplicaciones y el puesto 31 693 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 667 suscriptores.

Según los últimos datos del 08 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 150, y en las últimas 24 horas de 4, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.97%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.27% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 089 visualizaciones. En el primer día suele acumular 310 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 09 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 Tecnologías y Aplicaciones.

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Suscriptores

+424 horas

+437 días

+15030 días

1 089

Visitas de la publicación

~ 31024 horas

~ 45848 horas

7.97%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Data Structures in R

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An Artificial Neuron

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Layers of AI

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📚 Data Science Riddle What metric is commonly used to decide splits in decision trees?

Anonymous voting

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7 In Demand Data Analytics Skills

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Essential Pandas Methods For Data Science

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📚 Data Science Riddle In PCA, what do eigenvectors represent?

Anonymous voting

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AI Agents Quick Guide

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📚 Data Science Riddle Which algorithm groups data into clusters without labels?

Anonymous voting

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Extracting Features from Text - A Step-by-Step NLP Guide.pdf8.32 KB

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Dropout Explained Simply Neural networks are notorious for overfitting ( they memorize training data instead of generalizing). One of the simplest yet most powerful solutions? Dropout. During training, dropout randomly “drops” a percentage of neurons ( 20–50%). Those neurons temporarily go offline, meaning their activations aren’t passed forward and their weights aren’t updated in that round. 👉 What this does: ✔️ Forces the network to avoid relying on any single path. ✔️ Creates redundancy → multiple neurons learn useful features. ✔️ Makes the model more robust and less sensitive to noise. When testing happens, dropout is turned off, and all neurons fire but now they collectively represent stronger, generalized patterns. Imagine dropout like training with handicaps. It’s as if your brain had random “short blackouts” while studying, forcing you to truly understand instead of memorizing. And that’s why dropout remains a go-to regularization technique in deep learning and even in advanced architectures.

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Importance of Statistics and Exploratory Data Analysis

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What is RAG? 🤖📚 RAG stands for Retrieval-Augmented Generation. It’s a technique where an AI model first retrieves relevant info (like from documents or a database), and then generates an answer using that info. 🧠 Think of it like this: Instead of relying only on what it "knows", the model looks things up first - just like you would Google something before replying. 🔍 Retrieval + 📝 Generation = Smarter, up-to-date answers!

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Repost from Data visualization

How Data Science Roles are Changing With The Rise of AI

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📚 Data Science Riddle You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?

Anonymous voting

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Morning brain teaser! 🧠 Let's see who's awake... 📚 Data Science Riddle You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?

Anonymous voting

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Linear Algebra for Data Science.pdf6.12 KB

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🚀 Fast-Track Machine Learning Roadmap 2025 Mindset: Build first, learn just-in-time. Share progress publicly (GitHub + posts). Consistency > cramming. Weeks 1–2: Master Python, NumPy, Pandas, EDA, and data cleaning. Mini-win: load CSVs, handle missing data. Weeks 3–6: Learn ML fundamentals with scikit-learn — train/test splits, cross-validation, classifiers (LogReg, RF, XGB), and regressors. Project: spam classifier or house price predictor. Weeks 7–10: Dive into deep learning — tensors, autograd, PyTorch. Build CNN or text classifier + track experiments (Weights & Biases). Weeks 11–12: Specialize (NLP, CV, recommenders, MLOps) and ship a niche AI app. ———————— Weekly Routine: Mon-Tue: Learn concept + code example Wed-Thu: Build feature + log metrics Fri: Refactor + README + demo Sat: Share + get feedback + plan fixes Sun: Rest & review ———————— Portfolio Tips: Clear READMEs, reproducible env, demo videos, honest metric analysis. Avoid “math purgatory” and messy repos. Ship small every week! ———————— This approach gets you practical, portfolio-ready ML skills in ~3-4 months with real projects and solid evaluation for 2025 job markets!

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3 Types of Machine Learning