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.

13 667

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

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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

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Top Data Science Tools By Function

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📚 Data Science Riddle A business team wants interpretable insights, not just predictions. What's the best model to start with?

Anonymous voting

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Notes on SQL for data management and analysis, including queries and integration with R, from University of South Carolina.

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Top 6 Types of AI Models

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📚 Data Science Riddle Why might your SQL join explode the number of rows unexpectedly?

Anonymous voting

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Skills Needed To Become Data Analyst

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

This is our latest post from Instagram page, saved as PDF. If you want a very comprehensive breakdown on what's LLMs are and how they actually work, you might want to check it out. Here's our Instagram post: Explaining LLMs

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Regularization: The Art of Keeping Models Humble Overfitting is the “ego problem” of models. They memorize training data and forget how to generalize. Regularization is how we humble them. ➡️ L1 (Lasso): Shrinks some weights to zero → performs feature selection. ➡️ L2 (Ridge): Reduces all weights slightly → smooths learning. ➡️ Dropout: Randomly removes neurons during training → prevents co-dependence. It’s not about punishment but it’s about discipline. Regularization teaches models to focus on patterns, not exceptions. 💭 Remember: The best models don’t just fit data. They respect uncertainty.

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📚 Data Science Riddle You discover your regression model performs poorly on recent data. The relationships between variables have shifted. What's this called?

Anonymous voting

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List of AI Project Ideas 👨🏻‍💻 Beginner Projects 🔹 Sentiment Analyzer 🔹 Image Classifier 🔹 Spam Detection System 🔹 Face Detection 🔹 Chatbot (Rule-based) 🔹 Movie Recommendation System 🔹 Handwritten Digit Recognition 🔹 Speech-to-Text Converter 🔹 AI-Powered Calculator 🔹 AI Hangman Game Intermediate Projects 🔸 AI Virtual Assistant 🔸 Fake News Detector 🔸 Music Genre Classification 🔸 AI Resume Screener 🔸 Style Transfer App 🔸 Real-Time Object Detection 🔸 Chatbot with Memory 🔸 Autocorrect Tool 🔸 Face Recognition Attendance System 🔸 AI Sudoku Solver Advanced Projects 🔺 AI Stock Predictor 🔺 AI Writer (GPT-based) 🔺 AI-powered Resume Builder 🔺 Deepfake Generator 🔺 AI Lawyer Assistant 🔺 AI-Powered Medical Diagnosis 🔺 AI-based Game Bot 🔺 Custom Voice Cloning 🔺 Multi-modal AI App 🔺 AI Research Paper Summarizer

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🚨 When & How Jupyter Notebooks Fail (And What To Use Instead) Hey Data Folks! 👩‍💻👨‍💻 Let’s talk about Jupyter Notebooks — powerful for exploration, but risky in production. Here’s why: ❌ Problems with Notebooks: 1. Out-of-order execution → hidden bugs. 2. Code changes after execution → inconsistent results. 3. Data leakage → sensitive info in outputs. 4. Security risks → tokens/keys exposed. 5. Hard to apply engineering practices → no modular code, testing, CI/CD. 6. Collaboration pain → merge conflicts, JSON issues. 7. Reproducibility issues → missing dependencies, versions. ✅ When They’re Useful: - Quick data exploration & prototyping. - Knowledge sharing (clean, runnable from top to bottom). - Teaching / hands-on tutorials (with solution notebooks). 🔧 What to Use Instead: - For production code → .py files + IDEs. - For workflows → template repos & reproducible setups. - For deployment → MLOps tools, pipelines, automation. 💡 Key Takeaways: - Use notebooks for exploration & teaching. - Use structured code + pipelines for production & deployment. - Always document dependencies, keep notebooks clean, never commit secrets!

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📚 Data Science Riddle Your batch ETL job runs slower each week despite no code change. What's your first suspect?

Anonymous voting

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Pandas Cheatsheet For Data Analysis

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Hey everyone 👋 Some time ago, I asked if I should start a Data Science educational series and since 96% of you said yes, I began creating it. But many of you also asked for real, hands-on experience with projects, not just lessons. So I decided to shift gears. It’s now becoming a full practical coding course! 💻 My goal is to help you build skills that get you job-ready, not just teach theory. It’s taking a bit longer, but I promise it’ll be worth it. Thank you all for your support and patience ❤️ I’ll let you know as soon as we’re ready to start!

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📚 Data Science Riddle During EDA(Explanatory Data Analysis), what's the main reason we use box plots?

Anonymous voting

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Discusses Modeling ETL workflows for data warehousing, including data sources and transformations, from Drexel University.

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📚 Data Science Riddle Why is data validation before model training critical in production ML systems?

Anonymous voting

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AI Engineer Roadmap