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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Network:π Analytical overview of Telegram channel Machine Learning & Artificial Intelligence | Data Science Free Courses
Channel Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) in the English language segment is an active participant. Currently, the community unites 68 677 subscribers, ranking 2 392 in the Education category and 410 in the Malaysia region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 677 subscribers.
According to the latest data from 01 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 177 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.49%. Within the first 24 hours after publication, content typically collects 0.81% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 712 views. Within the first day, a publication typically gains 557 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as sellerflash, waybienad, pricing, buybox, buyer.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βPerfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfunβ
Thanks to the high frequency of updates (latest data received on 02 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
68 677
Subscribers
+924 hours
+77 days
+17730 days
Posts Archive
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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 π
. 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
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