Machine Learning & Artificial Intelligence | Data Science Free Courses
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频道 Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 68 668 名订阅者,在 教育 类别中位列第 2 389,并在 马来西亚 地区排名第 411 位。
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自 невідомо 创建以来,项目保持高速增长,吸引了 68 668 名订阅者。
根据 30 十月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 230,过去 24 小时变化为 3,整体触达仍然可观。
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作者将该频道定位为表达主观观点的平台:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
凭借高频更新(最新数据采集于 31 十月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。
68 668
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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
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SQL for Data Analysis
Автор: Cathy Tanimura
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