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Machine Learning & Artificial Intelligence | Data Science Free Courses

Machine Learning & Artificial Intelligence | Data Science Free Courses

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Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 672 obunachidan iborat bo'lib, TaΚΌlim toifasida 2 392-o'rinni va Malayziya mintaqasida 410-o'rinni egallagan.

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31 Oktabr, 2026 dagi oxirgi ma’lumotlarga koβ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 188 ga, soβ€˜nggi 24 soatda esa -24 ga oβ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

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Yuqori yangilanish chastotasi (oxirgi ma’lumot 01 Noyabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni TaΚΌlim toifasidagi muhim ta’sir nuqtasiga aylantirishini koβ€˜rsatadi.

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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 πŸ˜…. 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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