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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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📈 Аналітичний огляд Telegram-каналу Machine Learning & Artificial Intelligence | Data Science Free Courses

Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 672 підписників, посідаючи 2 392 місце в категорії Освіта та 410 місце у регіоні Малайзія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 672 підписників.

За останніми даними від 31 жовтня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 188, а за останні 24 години на -24, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.42%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.81% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 1 660 переглядів. Протягом першої доби публікація в середньому набирає 557 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 4.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як sellerflash, waybienad, pricing, buybox, buyer.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun”

Завдяки високій частоті оновлень (останні дані отримано 01 листопада, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

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68 672
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-2424 години
+117 днів
+18830 днів
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🔰 Complete SQL + Databases Bootcamp ⏱ 24.5 Hours 📦 278 Lessons Most comprehensive resource online to learn SQL and Database
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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.

Python Machine Learning .pdf1.42 MB

50 SQL Interview questions

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Statistics For Data Science

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SQL for Data Analysis Автор: Cathy Tanimura

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