¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 Análisis del canal de Telegram Machine Learning
El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 040 suscriptores, ocupando la posición 3 406 en la categoría Tecnologías y Aplicaciones y el puesto 232 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 040 suscriptores.
Según los últimos datos del 22 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 372, y en las últimas 24 horas de 2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 775 visualizaciones. En el primer día suele acumular 466 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 23 junio, 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 Tecnologías y Aplicaciones.
40 040
Suscriptores
+224 horas
+237 días
+37230 días
Archivo de publicaciones
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📌 Lasso Regression: Why the Solution Lives on a Diamond
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-23 | ⏱️ Read time: 24 min read
It’s simpler than you think.
#DataScience #AI #Python
40 041
📌 Your Synthetic Data Passed Every Test and Still Broke Your Model
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-23 | ⏱️ Read time: 11 min read
The silent gaps in synthetic data that only show up when your model is already…
#DataScience #AI #Python
40 041
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
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40 041
📌 I Simulated an International Supply Chain and Let OpenClaw Monitor It
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-23 | ⏱️ Read time: 9 min read
Mario asked me why 18% of his shipments were late when every team hit their…
#DataScience #AI #Python
40 041
📌 Using a Local LLM as a Zero-Shot Classifier
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-23 | ⏱️ Read time: 8 min read
A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted…
#DataScience #AI #Python
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📌 How to Run OpenClaw with Open-Source Models
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-22 | ⏱️ Read time: 8 min read
Run OpenClaw assistant through alternative LLMs
#DataScience #AI #Python
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Today, the public mint for Lobsters on TON goes live on Getgems 🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
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📌 Ivory Tower Notes: The Methodology
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-22 | ⏱️ Read time: 6 min read
A short intro to scientific methodology to combat “prompt in, slop out”
#DataScience #AI #Python
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📌 From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-22 | ⏱️ Read time: 8 min read
How I turned LLM persona interviews into a repeatable customer research workflow
#DataScience #AI #Python
40 041
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
👉 Join for Free, Click here
#ad 📢 InsideAd
40 041
11 Plots Data Scientists Use 90% of the Time 📊🚀
Here’s the secret → Data scientists don’t actually use 100+ types of charts. 🤫
When real decisions are on the line, it always comes back to the same 11.
https://t.me/DataScienceM
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📌 Correlation vs. Causation: Measuring True Impact with Propensity Score Matching
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-22 | ⏱️ Read time: 12 min read
Learn how Propensity Score Matching uncovers true causality in observational data. By finding “statistical twins,”…
#DataScience #AI #Python
40 041
📌 Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-22 | ⏱️ Read time: 19 min read
Turning free-to-use data into a hypothesis-ready dataset
#DataScience #AI #Python
40 041
📌 Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-21 | ⏱️ Read time: 15 min read
As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a…
#DataScience #AI #Python
40 041
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
👉 Join for Free, Click here
#ad 📢 InsideAd
40 041
📌 I Replaced GPT-4 with a Local SLM and My CI/CD Pipeline Stopped Failing
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-21 | ⏱️ Read time: 13 min read
The hidden cost of probabilistic outputs in systems that demand reliability
#DataScience #AI #Python
40 041
Repost from Machine Learning with Python
🔥 Google Colab has added the option of retraining 500+ open-source neural networks
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
