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Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 813 suscriptores, ocupando la posición 2 416 en la categoría Educación y el puesto 5 038 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.
Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 70, y en las últimas 24 horas de 10, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.44% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 997 visualizaciones. En el primer día suele acumular 1 652 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 10 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 Educación.
67 813
Suscriptores
+1024 horas
+127 días
+7030 días
Archivo de publicaciones
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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Gradio: The easiest way to demo your models.
- Core Idea: Quickly turn #ML models into interactive web apps.
- No frontend skills needed. It's all #Python.
- Works with any Python code, including custom functions.
- Share via temporary links or deploy on #HuggingFace Spaces.
- Get user feedback to improve your models.
If you're looking to create interactive demos for your ML project, check out #Gradio!
♻️ Repost if you found this useful
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📕 A Course in Reinforcement Learning by Dimitri P. Bertsekas
Explore the comprehensive world of Reinforcement Learning (RL) with this authoritative textbook by Dimitri P. Bertsekas. This book offers an in-depth overview of RL methodologies, focusing on optimal and suboptimal control, as well as discrete optimization. It's an essential resource for students, researchers, and professionals in the field.
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A Complete Course to Learn Robotics and Perception
Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson
github.com/gtbook/robotics
roboticsbook.org/intro.html
#Robotics #Perception #AI #DeepLearning #ComputerVision #RoboticsCourse #MachineLearning #Education #RoboticsResearch #GitHub⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
📂 8 Steps to Mastering MLOps
✅ For data scientists
⏯️ Introduction to MLOps
📎 MLOps Zoomcamp
📎 Neptune Blog
➖➖➖➖➖➖
2️⃣ Model Management
📎 ML Model Registry
📎 ML Experiment Tracking
📎 Experiment Tracking
➖➖➖➖➖➖
3️⃣ Building a pipeline of models
📎 Building End-to-End ML Pipelines
📎 Orchestration Tools
📎 Orchestration & ML Pipelines
➖➖➖➖➖➖
4️⃣ Monitoring models
📎 Evidently AI Blog
📎 NannyML Blog
📎 Model Monitoring
➖➖➖➖➖➖
5️⃣ Introduction to Docker
📎 Docker Tutorial
➖➖➖➖➖➖
6️⃣ Designing ML systems
📎 Designing ML Systems
📎 ML System Design Patterns
📎 ML System Design Interview
➖➖➖➖➖➖
7️⃣ Sample projects
📎 Evidently AI Database
📎 LLMOps Case Studies
➖➖➖➖➖➖
8️⃣ Comprehensive roadmap
📎 MLOps Roadmap 2024
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Open Guide to Data Structures and Algorithms
A must-read for anyone starting their journey in computer science and programming. This open-access book offers a clear, beginner-friendly introduction to the core concepts of data structures and algorithms, with simple explanations and practical examples. Whether you're a student or a self-learner, this guide is a solid foundation to build your DSA knowledge. Highly recommended for those who want to learn efficiently and effectively.
Read it here:
https://pressbooks.palni.org/anopenguidetodatastructuresandalgorithms
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Datasets Guide 📚
A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.
Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.
Link: https://docs.unsloth.ai/basics/datasets-guide
#MachineLearning #DeepLearning #Datasets #DataScience #AI #Unsloth #LLM #TrainingData #MLGuide
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Master PyTorch Faster with These Free Resources!
Whether you're just getting started with PyTorch or looking to refresh your deep learning skills, these two resources are all you need:
1. PyTorch Cheatsheet
A concise reference guide packed with essential PyTorch commands and patterns. Perfect for quick look-ups during development.
Download:
https://www.dropbox.com/scl/fi/e4xngykrfoubiw3xnd6fz/PyTorch-Cheatsheet.pdf?rlkey=vgx38ckps7aie120imgozgq4g&e=2&st=hgs06d4t&dl=0
2. Learn PyTorch Deep Learning with Hands-On Code
A beginner-friendly PDF with practical examples to help you build and train deep learning models using PyTorch from scratch.
Download:
https://www.dropbox.com/scl/fi/lfo7r6fnd8wjm3gp0jteh/Learn-PyTorch-Deep-Learning-with-Hands-On-Code.pdf?rlkey=mg9cxg41yerouzp0rklm8hqa2&e=2&st=c7k7rgay&dl=0
Save them, share them, and start building smarter models today!
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Foundations of Large Language Models
Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf
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