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Machine Learning & Artificial Intelligence | Data Science Free Courses

Machine Learning & Artificial Intelligence | Data Science Free Courses

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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun

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📈 Аналитический обзор Telegram-канала Machine Learning & Artificial Intelligence | Data Science Free Courses

Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 672 подписчиков, занимая 2 392 место в категории Образование и 410 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 672 подписчиков.

Согласно последним данным от 31 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 188, а за последние 24 часа — -24, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.42%. В первые 24 часа после публикации контент обычно набирает 0.81% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 1 660 просмотров. В течение первых суток публикация набирает 557 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, waybienad, pricing, buybox, buyer.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun”

Благодаря высокой частоте обновлений (последние данные получены 01 ноября, 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 Образование.

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🔰 Complete SQL + Databases Bootcamp ⏱ 24.5 Hours 📦 278 Lessons Most comprehensive resource online to learn SQL and Database
🔰 Complete SQL + Databases Bootcamp ⏱ 24.5 Hours 📦 278 Lessons Most comprehensive resource online to learn SQL and Database Management & Design + exercises to give you real-world experience working with all database types. Taught By: Mo Binni, Andrei Neagoie Download Full Course: https://t.me/sqlanalyst/38 Download All Courses: https://t.me/sqlspecialist

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Vital Cheat sheets for Data Scientists and Machine Learning Engineers

Prompt Engineering in itself does not warrant a separate job. Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT. You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc. The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.

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SQL for Data Analysis Автор: Cathy Tanimura

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