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 Индия31 743 Технологии и приложения9 391...

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

Канал Data science/ML/AI (@datascience_bds) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 13 667 подписчиков, занимая 9 391 место в категории Технологии и приложения и 31 743 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 13 667 подписчиков.

Согласно последним данным от 08 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 150, а за последние 24 часа — 4, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 7.97%. В первые 24 часа после публикации контент обычно набирает 2.27% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 089 просмотров. В течение первых суток публикация набирает 310 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 09 июня, 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.

13 667

Подписчики

+424 часа

+437 дней

+15030 день

1 089

Просмотры поста

~ 31024 часа

~ 45848 часов

7.97%

Коэффициент вовлеченности

~ 1

Постов в день

Ads index

beta

Архив постов

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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?

Anonymous voting

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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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