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 743 Tecnologías y Aplicaciones9 391...

📈 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 9 391 en la categoría Tecnologías y Aplicaciones y el puesto 31 743 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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Hey everyone 👋 Tomorrow we are kicking off a new short & free series called: 📊 Data Importing Series 📊 We’ll go through all the real ways to pull data into Python: → CSV, Excel, JSON and more → Databases & SQL databases → APIs, Google Sheets, even PDFs & web scraping Short lessons, ready-to-copy code, zero boring theory. First part drops tomorrow. Turn on notifications so you don’t miss it 🔔 Who’s excited? React with a 🔥 if you are.

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Normalization vs Standardization: Why They’re Not the Same People treat these two as interchangeable. they’re not. 👉 Normalization (Min-Max scaling): Compresses values to 0–1. Useful when magnitude matters (pixel values, distances). 👉 Standardization (Z-score): Centers data around mean=0, std=1. Useful when distribution shape matters (linear/logistic regression, PCA). 🔑 Key idea: Normalization preserves relative proportions. Standardization preserves statistical structure. Pick the wrong one, and your model’s geometry becomes distorted.

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📚 Data Science Riddle - CNN Kernels Which convolution increases channel depth but not spatial size?

Anonymous voting

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✅ Complete AI (Artificial Intelligence) Roadmap 🤖🚀 1️⃣ Basics of AI 🔹 What is AI? 🔹 Types: Narrow AI vs General AI 🔹 AI vs ML vs DL 🔹 Real-world applications 2️⃣ Python for AI 🔹 Python syntax & libraries 🔹 NumPy, Pandas for data handling 🔹 Matplotlib, Seaborn for visualization 3️⃣ Math Foundation 🔹 Linear Algebra: Vectors, Matrices 🔹 Probability & Statistics 🔹 Calculus basics 🔹 Optimization techniques 4️⃣ Machine Learning (ML) 🔹 Supervised vs Unsupervised 🔹 Regression, Classification, Clustering 🔹 Scikit-learn for ML 🔹 Model evaluation metrics 5️⃣ Deep Learning (DL) 🔹 Neural Networks basics 🔹 Activation functions, backpropagation 🔹 TensorFlow / PyTorch 🔹 CNNs, RNNs, LSTMs 6️⃣ NLP (Natural Language Processing) 🔹 Text cleaning & tokenization 🔹 Word embeddings (Word2Vec, GloVe) 🔹 Transformers & BERT 🔹 Chatbots & summarization 7️⃣ Computer Vision 🔹 Image processing basics 🔹 OpenCV for CV tasks 🔹 Object detection, image classification 🔹 CNN architectures (ResNet, YOLO) 8️⃣ Model Deployment 🔹 Streamlit / Flask APIs 🔹 Docker for containerization 🔹 Deploy on cloud: Render, Hugging Face, AWS 9️⃣ Tools & Ecosystem 🔹 Git & GitHub 🔹 Jupyter Notebooks 🔹 DVC, MLflow (for tracking models) 🔟 Build AI Projects 🔹 Chatbot, Face recognition 🔹 Spam classifier, Stock prediction 🔹 Language translator, Object detector

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📚 Data Science Riddle Your model's loss fluctuates but doesn't decrease overall. What's the most likely issue?

Anonymous voting

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The Difference Between Model Accuracy and Business Accuracy A model can be 95% accurate… yet deliver 0% business value. Why❔ Because data science metrics ≠ business metrics. 📌 Examples: - A fraud model catches tiny fraud but misses large ones - A churn model predicts already obvious churners - A recommendation model boosts clicks but reduces revenue Always align ML metrics with business KPIs. Otherwise, your “great model” is just a great illusion.

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📚 Data Science Riddle Your estimate has high variance. Best fix?

Anonymous voting

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

Covers Spark for ML, graph processing (GraphFrames), and integration with Hadoop from Stanford University.

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📚 Data Science Riddle A feature has low importance but domain experts insist it matters. What do you do?

Anonymous voting

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6 Must-Know Data Engineering Tools For Beginners

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📚 Data Science Riddle You need fast reads of small files. What storage options fits best?

Anonymous voting

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🛠️ Running Code in Jupyter Notebooks Jupyter Notebooks let you write & run code interactively. Here’s a quick guide to make your workflow smoother: ▶️ Kernel & Code Cells - Each notebook is tied to a single kernel (e.g. IPython). - Code cells are where you write and execute code. ⌨️ Useful Shortcuts - Shift + Enter → run current cell, move to next - Alt + Enter → run current cell, insert new one below - Ctrl + Enter → run current cell, stay in place 🔄 Kernel Management - Interrupt the kernel if code hangs. - Restart kernel to reset memory & variables. 🖥️ Output Handling - Results & errors appear directly under the cell. - Long-running code outputs appear as they’re generated. - Large outputs can be scrolled or collapsed for clarity. 💡 Pro Tip: Always “Restart & Run All” before sharing or saving a notebook. This ensures reproducibility and clean results. 👉 Explore

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📚 Data Science Riddle You want to prevent inconsistent data across environments. What helps most?

Anonymous voting

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If you want to become a Data Scientist, this is the path to follow.

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The Big Data bible from Stanford: MapReduce, Spark, recommendation systems, PageRank, locality-sensitive hashing, Large scale machine learning and mining social networks/streams all explained clearly with real algorithms you can code today. 500 pages of pure gold.

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📚 Data Science Riddle You want to detect extreme values visually in one plot. Which one is best?

Anonymous voting

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Everything You need To Know About Databricks

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📚 Data Science Riddle A query runs slowly due to large table scans. What's the most targeted fix?

Anonymous voting

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The Simplest Machine Learning Cheatsheet

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📚 Data Science Riddle Two team members run the same notebook but get different results. What's the culprit?

Anonymous voting