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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 Siria211 Tecnologías y Aplicaciones3 207...

📈 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 41 884 suscriptores, ocupando la posición 3 207 en la categoría Tecnologías y Aplicaciones y el puesto 211 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 41 884 suscriptores.

Según los últimos datos del 26 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 467, y en las últimas 24 horas de 3, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.28%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.07% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 211 visualizaciones. En el primer día suele acumular 866 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
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 27 octubre, 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.

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41 884

Suscriptores

+324 horas

+527 días

+46730 días

2 211

Visitas de la publicación

~ 86624 horas

~ 1 18248 horas

5.28%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

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The 25 Best Free Resources for Learning AI in 2026 Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers. Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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Repost from Machine Learning with Python

🔖 637 interactive modules on ML and AI On VizLearn, you can visually explore: 🫡 Gradient descent; 🫡 SVM and PCA; 🫡 Bayes' theorem; 🫡 BPE tokenization; 🫡 Query, Key, and Value; 🫡 KV cache; 🫡 Quantization; 🫡 Neural networks and LLMs. Change the input data and immediately see the results of the calculations. ⛓ Link to the tutorial https://vizlearn.in/

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🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees. 📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers → https://bit.ly/4yrk1Ti 🪜 Online Free Course — structured path, works as a refresher before exams → https://bit.ly/4hUprRP ☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks → https://bit.ly/4yt1iXy 📊 Cloud Study Guide — cheat-sheet style, handy for interview prep → https://bit.ly/4el4Eod 🧠 Free Mock Exam — test yourself before booking the real thing → https://bit.ly/4sbWlkl 📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪 🌐 Community: https://chat.whatsapp.com/CS7VOvQCLZG5BJPazBGm1v 📲 Personalized help: https://wa.link/xd2fu6 #Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers

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📚 7 Best Websites to Learn Computer Science Subjects for Free More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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Repost from Machine Learning with Python

https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8 Follow me to more posts and useful content

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Repost from Data Analytics

You can literally enter a sentence and see how the Transformer processes it. This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process: Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token. You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation. If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this. https://poloclub.github.io/transformer-explainer/ https://t.me/DataAnalyticsX

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🚀 RESEARCH & DIGITAL SOLUTIONS — YOUR PARTNER IN SUCCESS! Are you a researcher, student, business owner, or developer looking for professional support? We are here to help you turn your ideas into real results! 💡 🔬 1. Scientific Research & Academic Publishing Professional review and evaluation of scientific manuscripts before submission. Guidance on journal selection and publication strategies. Expert advice to improve research quality and presentation. Support for researchers throughout the academic publishing process. 💻 2. Website & Software Development Professional website design and development for businesses and individuals. Custom electronic systems tailored to your needs. Technical assistance for students and academic projects. ⚙️ 3. API & User Interface (UI) Services API development and integration. Modern, responsive, and user-friendly interface design. Seamless integration between applications and systems. 📊 4. Real-World & Synthetic Datasets Providing real-world datasets for research and analysis. Creating customized synthetic datasets according to your requirements. Data support for scientific research, machine learning, and AI projects. 🤝 5. FREE Discussion & Consultation Discuss your research ideas and technical projects with us. Get initial guidance and recommendations. Explore the best solutions for your academic or professional needs. 🌟 WHY CHOOSE US? ✅ Research and technology expertise in one place. ✅ Tailored solutions based on your needs. ✅ Professional guidance and practical support. ✅ Free initial discussion and consultation. 📩 CONTACT US ON TELEGRAM 👨‍💻 Team Admin & Support: @Omidyzd62 💬 Feel free to contact us to discuss your ideas, ask questions, or explore how we can help! Your Research. Your Ideas. Our Expertise. Your Success. 🎯 #ResearchSupport #AcademicPublishing #ScientificResearch #WebDevelopment #SoftwareDevelopment #API #UIUX #Datasets #MachineLearning #ArtificialIntelligence #StudentSupport

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🚀 ChatGPT Plus & Gemini AI Pro🤖 ChatGPT Plus — 1 Month: $8 ✨ Gemini AI Pro — 18 Months: $2.50 🧠 Claude API ⚡ Fast delivery 🌍 Worldwide 🔐 Directly via Telegram 👉 https://t.me/Hppykeys_BOT?start=arb0910

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Tracking experiments and versioning ML models with MLflow. 🤖 Machine learning development requires saving hyperparameters, metrics, and artifacts for each run to compare results. The MLflow platform logs key metrics and registers trained models in a central registry. We will install MLflow, run a script to log parameters, and register the model. Let's install the

mlflow

and

scikit-learn

libraries to conduct and log a test experiment. 📦

pip install mlflow scikit-learn

The dependencies for managing ML experiments have been successfully installed. ✅ Now, let's create a Python script called

train.py

that will train a simple model, log metrics, and save it to MLflow. 🐍

import mlflow
from sklearn.ensemble import RandomForestClassifier

mlflow.set_experiment("demo_experiment")
with mlflow.start_run():
    params = {"n_estimators": 100, "max_depth": 5}
    mlflow.log_params(params)
    model = RandomForestClassifier(**params)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "rf_model")

The training script and metric logging are set up and ready to be executed. 🚀 Let's run the training script to capture the results and parameters in the local MLflow storage.

python3 train.py

The experiment has been successfully completed, and the parameters and model artifacts have been saved. 📊

# verification (check for registered runs in MLflow)
mlflow runs list --experiment-name demo_experiment

Expected output:

demo_experiment ... FINISHED

# cleanup (remove the generated directory with artifacts and the script)
rm -rf mlruns train.py

Using MLflow helps avoid chaos when tuning hyperparameters and ensures reproducibility of results. Deploy an MLflow server on a separate host for the entire team to collaborate on the project. 🌐 #MLflow #MachineLearning #Python #DataScience #MLOps #AI ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Repost from Machine Learning with Python

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100 Machine Learning Interview Questions and Answers

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Repost from Udemy Free

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Repost from Machine Learning with Python

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