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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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Programming, data science, ML - free courses by Big Data SpecialistСеть:Programming, data science, ML - free courses by Big Data SpecialistИндия31 743Технологии 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
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+437 дней
+15030 день
Архив постов
Data science/ML/AI
Data science/ML/AI
13 667
📚 Data Science Riddle Why do CNNs use pooling layers?
Anonymous voting

Data science/ML/AI
Data science/ML/AI
13 667
Why is Kafka Called Kafka❔ Here’s a fun fact that surprises a lot of people. The “Kafka” you use for real-time data pipelines
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Why is Kafka Called Kafka❔ Here’s a fun fact that surprises a lot of people. The “Kafka” you use for real-time data pipelines is… named after the novelist Franz Kafka. Why? Jay Kreps (the creator) once explained it simply: - He liked the name. - It sounded mysterious. - And Kafka (the author) wrote a lot. That last part is key. Because Apache Kafka is all about writing: streams of events, logs, and data in motion. So the name stuck. Today, Millions of engineers across the globe talk about “Kafka” every single day… and most don’t realize they’re also invoking a 20th-century novelist. It's funny how small choices like naming your project can shape how the world remembers it.

Data science/ML/AI
Data science/ML/AI
13 667
Cheatsheet: Bayes Theroem And Classifier
Cheatsheet: Bayes Theroem And Classifier

Data science/ML/AI
Data science/ML/AI
13 667
Important LLM Terms 🔹 Transformer Architecture 🔹 Attention Mechanism 🔹 Pre-training 🔹 Fine-tuning 🔹 Parameters 🔹 Self-A
Important LLM Terms 🔹 Transformer Architecture 🔹 Attention Mechanism 🔹 Pre-training 🔹 Fine-tuning 🔹 Parameters 🔹 Self-Attention 🔹 Embeddings 🔹 Context Window 🔹 Masked Language Modeling (MLM) 🔹 Causal Language Modeling (CLM) 🔹 Multi-Head Attention 🔹 Tokenization 🔹 Zero-Shot Learning 🔹 Few-Shot Learning 🔹 Transfer Learning 🔹 Overfitting 🔹 Inference 🔹 Language Model Decoding 🔹 Hallucination 🔹 Latency

Data science/ML/AI
Data science/ML/AI
13 667
📚 Data Science Riddle In a medical diagnosis project, what's more important?
Anonymous voting

Data science/ML/AI
Data science/ML/AI
13 667
Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻 It's easy with Telega.io. As the leadi
Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻 It's easy with Telega.io. As the leading platform for native ads and integrations on Telegram, it provides user-friendly and efficient tools for quick and automated ad launches. ⚡️ Place your ad here in three simple steps: 1 Sign up 2 Top up the balance in a convenient way 3 Create your advertising post If your ad aligns with our content, we’ll gladly publish it. Start your promotion journey now!

Data science/ML/AI
Data science/ML/AI
13 667
ML models don’t all think alike 🤖 ❇️ Naive Bayes = probability ❇️ KNN = proximity ❇️ Discriminant Analysis = decision bounda
+2
ML models don’t all think alike 🤖 ❇️ Naive Bayes = probability ❇️ KNN = proximity ❇️ Discriminant Analysis = decision boundaries Different paths, same goal: accurate classification. Which one do you reach for first?

Data science/ML/AI
Data science/ML/AI
13 667
📚 Data Science Riddle A dataset has 20% missing values in a critical column. What's the most practical choice?
Anonymous voting

Data science/ML/AI
Data science/ML/AI
13 667
Introduction To Linear Regression
Introduction To Linear Regression

Data science/ML/AI
Data science/ML/AI
13 667
SQL JOINS
SQL JOINS

Data science/ML/AI
Data science/ML/AI
13 667
📚 Data Science Riddle Which Metric is best for imbalanced classification?
Anonymous voting

Data science/ML/AI
Data science/ML/AI
13 667
Machine Learning Cheatsheet
Machine Learning Cheatsheet

Data science/ML/AI
Data science/ML/AI
13 667
Most Common Data Science Skills in Job Posting
Most Common Data Science Skills in Job Posting

Data science/ML/AI
Data science/ML/AI
13 667
📊 Infographic Elements That Every Data Person Should Master 🚀 After years of working with data, I can tell you one thing: �
📊 Infographic Elements That Every Data Person Should Master 🚀 After years of working with data, I can tell you one thing: 👉 The chart ou choose is as important as the data itself. Here’s your quick visual toolkit 👇 🔹 Timelines * Sequential ⏩ great for processes * Scaled ⏳ best for real dates/events 🔹 Circular Charts * Donut 🍩 & Pie 🥧 for proportions * Radial 🌌 for progress or cycles * Venn 🎯 when you want to show overlaps 🔹 Creative Comparisons * Bubble 🫧 & Area 🔵 for impact by size * Dot Matrix 🔴 for colorful distributions * Pictogram 👥 when storytelling matters most 🔹 Classic Must-Haves * Bar 📊 & Histogram 📏 (clear, reliable) * Line 📈 for trends * Area 🌊 & Stacked Area for the “big picture” 🔹 Advanced Tricks * Stacked Bar 🏗 when categories add up * Span 📐 for ranges * Arc 🌈 for relationships 💡 Pro tip from experience: If your audience doesn’t “get it” in 3 seconds, change the chart. The best visualizations speak louder than numbers

Data science/ML/AI
Data science/ML/AI
13 667
INFOGRAPHIC ELEMENTS
INFOGRAPHIC ELEMENTS

Data science/ML/AI
Data science/ML/AI
13 667
📚 Data Science Riddle Why does bagging reduce variance?
Anonymous voting

Data science/ML/AI
Data science/ML/AI
13 667
Big Data 5V
Big Data 5V

Data science/ML/AI
Data science/ML/AI
13 667
Great Packages for R
Great Packages for R

Data science/ML/AI
Data science/ML/AI
13 667
📚 Data Science Riddle Which algorithm is most sensitive to feature scaling?
Anonymous voting

Data science/ML/AI
Data science/ML/AI
13 667
The RAG Developer Stack 2025 - Build Intelligent Al That Thinks, Remembers & Acts
The RAG Developer Stack 2025 - Build Intelligent Al That Thinks, Remembers & Acts