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

Red:Machine Learning with Python Siria232 Tecnologías y Aplicaciones3 398...

📈 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 40 100 suscriptores, ocupando la posición 3 398 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 40 100 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 379, y en las últimas 24 horas de 30, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.92%. 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 770 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 24 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.

40 100

Suscriptores

+3024 horas

+337 días

+37930 días

770

Visitas de la publicación

~ 46624 horas

~ 62048 horas

1.92%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 105

📌 The Proximity of the Inception Score as an Evaluation Criterion 🗂 Category: DEEP LEARNING 🕒 Date: 2026-02-03 | ⏱️ Read time: 7 min read The neighborhood of synthetic data #DataScience #AI #Python

40 105

📌 Building Systems That Survive Real Life 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-02-02 | ⏱️ Read time: 4 min read Sara Nobrega on the transition from data science to AI engineering, using LLMs as a… #DataScience #AI #Python

40 105

📌 Silicon Darwinism: Why Scarcity Is the Source of True Intelligence 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-02 | ⏱️ Read time: 9 min read We are confusing “size” with “smart.” The next leap in artificial intelligence will not come… #DataScience #AI #Python

40 105

📌 Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-01 | ⏱️ Read time: 20 min read Leveraging massive parallelism, asynchronous updates, and multi-machine training to match and exceed human-level performance #DataScience #AI #Python

40 105

📌 How to Apply Agentic Coding to Solve Problems 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-31 | ⏱️ Read time: 7 min read Learn how to efficiently solve problems with coding agents #DataScience #AI #Python

40 105

📌 How to Run Claude Code for Free with Local and Cloud Models from Ollama 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-31 | ⏱️ Read time: 16 min read Ollama now offers Anthropic API compatibility #DataScience #AI #Python

40 105

📌 Multi-Attribute Decision Matrices, Done Right 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-30 | ⏱️ Read time: 7 min read How to structure decisions, identify efficient options, and avoid misleading value metrics #DataScience #AI #Python

40 105

📌 On the Possibility of Small Networks for Physics-Informed Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-30 | ⏱️ Read time: 20 min read A new kind of hyperparameter study #DataScience #AI #Python

40 105

📌 Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-30 | ⏱️ Read time: 27 min read Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy… #DataScience #AI #Python

40 105

📌 Creating an Etch A Sketch App Using Python and Turtle 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-30 | ⏱️ Read time: 7 min read A beginner-friendly Python tutorial #DataScience #AI #Python

40 105

📌 Randomization Works in Experiments, Even Without Balance 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-29 | ⏱️ Read time: 10 min read Randomization usually balances confounders in experiments, but what happens when it doesn’t? #DataScience #AI #Python

40 105

📌 The Unbearable Lightness of Coding 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-01-29 | ⏱️ Read time: 9 min read Confessions of a vibe coder #DataScience #AI #Python

40 105

📌 RoPE, Clearly Explained 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-29 | ⏱️ Read time: 8 min read Going beyond the math to build intuition #DataScience #AI #Python

40 105

📌 Optimizing Vector Search: Why You Should Flatten Structured Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-29 | ⏱️ Read time: 7 min read An analysis of how flattening structured data can boost precision and recall by up to 20% #DataScience #AI #Python

40 105

📌 Machine Learning in Production? What This Really Means 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-28 | ⏱️ Read time: 10 min read From notebooks to real-world systems #DataScience #AI #Python

40 105

📌 Federated Learning, Part 2: Implementation with the Flower Framework 🗂 Category: FEDERATED LEARNING 🕒 Date: 2026-01-28 | ⏱️ Read time: 11 min read Implementing cross-silo federated learning step by step #DataScience #AI #Python

40 105

📌 Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-28 | ⏱️ Read time: 12 min read Estimating neighborhood-level pedestrian risk from real-world incident data #DataScience #AI #Python

40 105

📌 I Ditched My Mouse: How I Control My Computer With Hand Gestures (In 60 Lines of Python) 🗂 Category: COMPUTER VISION 🕒 Date: 2026-01-28 | ⏱️ Read time: 9 min read A step-by-step guide to building a “Minority Report”-style interface using OpenCV and MediaPipe #DataScience #AI #Python

40 105

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40 105

Repost from Machine Learning with Python

💛 Top 10 Best Websites to Learn Machine Learning ⭐️ by [@codeprogrammer] --- 🧠 Google’s ML Course 🔗 https://developers.google.com/machine-learning/crash-course 📈 Kaggle Courses 🔗 https://kaggle.com/learn 🧑‍🎓 Coursera – Andrew Ng’s ML Course 🔗 https://coursera.org/learn/machine-learning ⚡️ Fast.ai 🔗 https://fast.ai 🔧 Scikit-Learn Documentation 🔗 https://scikit-learn.org 📹 TensorFlow Tutorials 🔗 https://tensorflow.org/tutorials 🔥 PyTorch Tutorials 🔗 https://docs.pytorch.org/tutorials/ 🏛️ MIT OpenCourseWare – Machine Learning 🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/ ✍️ Towards Data Science (Blog) 🔗 https://towardsdatascience.com --- 💡 Which one are you starting with? Drop a comment below! 👇 #MachineLearning #LearnML #DataScience #AI https://t.me/CodeProgrammer 🌟