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Red:📈 Análisis del canal de Telegram Machine Learning & Artificial Intelligence | Data Science Free Courses
El canal Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 68 672 suscriptores, ocupando la posición 2 392 en la categoría Educación y el puesto 410 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 672 suscriptores.
Según los últimos datos del 31 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 188, y en las últimas 24 horas de -24, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.42%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.81% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 660 visualizaciones. En el primer día suele acumular 557 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, waybienad, pricing, buybox, buyer.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 01 noviembre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
68 672
Suscriptores
-2424 horas
+117 días
+18830 días
Archivo de publicaciones
🔰 Complete SQL + Databases Bootcamp
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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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SQL for Data Analysis
Автор: Cathy Tanimura
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