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
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Red:📈 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 3 201 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 25 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 466, y en las últimas 24 horas de 7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.36%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.97% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 244 visualizaciones. En el primer día suele acumular 824 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 26 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.
41 884
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+724 horas
+497 días
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 26 octubre | +8 | |||
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| 12 octubre | +10 | |||
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| 04 octubre | +40 | |||
| 03 octubre | +13 | |||
| 02 octubre | +17 | |||
| 01 octubre | +27 |
Publicaciones del Canal
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
| 2 |  🔖 637 interactive modules on ML and AI
On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
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🫡 Quantization;
🫡 Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
⛓ Link to the tutorial
https://vizlearn.in/ | 747 |
| 3 |  🔥 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.
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| 4 | Agentic AI is the future.
If you want to become god-level with AI agents, save these 10 GitHub repos:
1 Awesome LLM Apps
🔗 https://github.com/Shubhamsaboo/awesome-llm-apps
2 Generative AI for Beginners
🔗 https://github.com/microsoft/generative-ai-for-beginners
3 LLM Course
🔗 https://github.com/mlabonne/llm-course
4 Prompt Engineering Guide
🔗 https://github.com/dair-ai/Prompt-Engineering-Guide
5 AI Agents for Beginners
🔗 https://github.com/microsoft/ai-agents-for-beginners
6 AI Agents Projects
🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects
7 Hugging Face Agents Course
🔗 https://github.com/huggingface/agents-course
8 Awesome AI Agents
🔗 https://github.com/e2b-dev/awesome-ai-agents
9 GenAI Agents
🔗 https://github.com/NirDiamant/GenAI_Agents
10 Agents Towards Production
🔗 https://github.com/NirDiamant/agents-towards-production
Anything else you'd add? | 4 879 |
| 5 | 📚 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 | 2 104 |
| 6 | https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8
Follow me to more posts and useful content | 1 379 |
| 7 |  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 | 1 805 |
| 8 |  Sin texto... | 1 938 |
| 9 |  Sin texto... | 4 665 |
| 10 |  🚀 RESEARCH & DIGITAL SOLUTIONS — YOUR PARTNER IN SUCCESS!
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| 12 | Sin texto... | 2 238 |
| 13 | 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. 🌐
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| 14 |  Sin texto... | 4 284 |
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| 16 | 100 Machine Learning Interview Questions and Answers | 3 680 |
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| 20 |  Sin texto... | 1 896 |
