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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The Data Analyst Cheatsheet

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Cheatsheet: Imbalanced Data In Classification

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📚 Data Science Riddle You're building a chatbot but it gives generic answers. What's the root issue?

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Top ML Interview Questions & Answers.pdf1.42 KB

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Phases To Master Agentic AI

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Data Drift: The reason Good Models Go Bad You built a model that performed amazingly last month. Now? Accuracy tanked. Confusion Matrix looks like a crime scene. Welcome to Data Drift. The silent model killer. 📉 What Is Data Drift? It’s when the data your model sees today is different from the data it was trained on. Imagine you trained a model on pre-COVID shopping data then you tried to predict online purchases in 2021. People’s behavior changed. Your model didn’t. That’s drift. Reality shifted, but your math stayed still. 🧠 The Core Types ➡️ Covariate Drift: Input features change (e.g., user age distribution shifts). ➡️ Prior Drift: The target variable’s frequency changes (e.g., fewer defaults now). ➡️ Concept Drift: The relationship between input and output changes entirely. The last one is deadly. your model’s logic literally stops making sense. 🚨 Why It’s Dangerous Models decay quietly. By the time you notice lower performance, the damage( business or otherwise ) is already done. That’s why top teams monitor models like systems, not code. 🧩 The Fix 1. Track feature distributions over time (use KS test, PSI, or histograms). 2. Monitor prediction confidence — sudden uncertainty = red flag. 3. Retrain models periodically with fresh data. AI isn’t “build once.” It’s “maintain forever.”

A model is only as good as the world it was trained in and the world never stops changing.

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Comprehensive Feature Engineering Techniques

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📚 Data Science Riddle You're classifying product reviews (positive/negative). Which feature method is more effective for capturing context?

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Parameters vs Hyperparameters People confuse these all the time. Parameters: learned by the model during training. (e.g., weights in a neural network, coefficients in regression). Hyperparameters: set before training. They control how the model learns. (e.g., learning rate, number of layers, batch size). ✔️ Parameters = the student’s knowledge (changes as they study). ✔️ Hyperparameters = the teacher’s instructions (fixed rules of how to study). Tuning hyperparameters is often the difference between a good model and a useless one.

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DSA Cheatsheet

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📚 Data Science Riddle In Naive Bayes, what's the "naive" assumption?

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📚 Data Science Riddle You're training a hiring model. What's the biggest ethical risk?

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Cheatsheet: Ensemble Learning in ML

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cheatsheet-deep-learning.pdf3.35 KB

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AI/ML Cheatsheet

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Artificial Intelligence for Learning.pdf2.76 MB

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Softmax vs Sigmoid Functions Two of the most common activation functions… and two of the most misunderstood. Sigmoid: squashes input into a range between 0 and 1. Perfect for binary classification (yes/no problems). Example: spam or not spam. Softmax: takes a vector of numbers and turns them into probabilities that sum to 1. Perfect for multi-class classification (cat vs dog vs horse). 👉 Rule of thumb: Binary task → use Sigmoid. Multi-class task → use Softmax. Simple, but if you get this wrong, your model will never make sense.

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Data Visualization Cheatsheet

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Data Analyst 🆚 Data Engineer: Key Differences Confused about the roles of a Data Analyst and Data Engineer? 🤔 Here's a breakdown: 👨‍💻 Data Analyst: 🎯 Role: Analyzes, interprets, & visualizes data to extract insights for business decisions. 👍 Best For: Those who enjoy finding patterns, trends, & actionable insights. 🔑 Responsibilities: 🧹 Cleaning & organizing data. 📊 Using tools like Excel, Power BI, Tableau & SQL. 📝 Creating reports & dashboards. 🤝 Collaborating with business teams. Skills: Analytical skills, SQL, Excel, reporting tools, statistical analysis, business intelligence. ✅ Outcome: Guides decision-making in business, marketing, finance, etc. ⚙️ Data Engineer: 🏗️ Role: Designs, builds, & maintains data infrastructure. 👍 Best For: Those who enjoy technical data management & architecture for large-scale analysis. 🔑 Responsibilities: 🗄️ Managing databases & data pipelines. 🔄 Developing ETL processes. 🔒 Ensuring data quality & security. ☁️ Working with big data technologies like Hadoop, Spark, AWS, Azure & Google Cloud. Skills: Python, Java, Scala, database management, big data tools, data architecture, cloud technologies. ✅ Outcome: Creates infrastructure & pipelines for efficient data flow for analysis. In short: Data Analysts extract insights, while Data Engineers build the systems for data storage, processing, & analysis. Data Analysts focus on business outcomes, while Data Engineers focus on the technical foundation.

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📚 Data Science Riddle Why do CNNs use pooling layers?

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