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

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Сеть:Programming, data science, ML - free courses by Big Data Specialist Индия28 900 Технологии и приложения8 924...

📈 Аналитический обзор Telegram-канала Data science/ML/AI

Канал Data science/ML/AI (@datascience_bds) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 13 901 подписчиков, занимая 8 924 место в категории Технологии и приложения и 28 900 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 13 901 подписчиков.

Согласно последним данным от 28 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 83, а за последние 24 часа — 0, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 7.62%. В первые 24 часа после публикации контент обычно набирает 2.09% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 059 просмотров. В течение первых суток публикация набирает 290 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как panda, learning, row, api, ethic.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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...”

Благодаря высокой частоте обновлений (последние данные получены 29 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

13 901

Подписчики

Нет данных24 часа

-47 дней

+8330 день

1 059

Просмотры поста

~ 29024 часа

~ 41348 часов

7.62%

Коэффициент вовлеченности

~ 1

Постов в день

Ads index

beta

Архив постов

13 907

Statistical Moments (M1, M2) for Data Analysis Here are 5 curated PDFs diving into the mean (M1), variance (M2), and their applications in crafting research questions and sourcing data. A channel member requested resources on this topic and we delivered. If you have a topic you want resources on let us know, and we’ll make it happen! @datascience_bds

13 907

📚 Data Science Riddle Model Accuracy improves after dropping half the features. Why?

Anonymous voting

13 907

The Data Analyst Cheatsheet

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Cheatsheet: Imbalanced Data In Classification

13 907

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

Anonymous voting

13 907

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.

13 907

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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13 907

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?

Anonymous voting

13 907

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

Anonymous voting

13 907

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.

13 907

Data Visualization Cheatsheet