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
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Red:📈 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 68 497 suscriptores, ocupando la posición 2 398 en la categoría Educación y el puesto 4 584 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 497 suscriptores.
Según los últimos datos del 30 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 44, y en las últimas 24 horas de -15, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.47% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 712 visualizaciones. En el primer día suele acumular 1 004 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
- 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 31 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 Educación.
68 497
Suscriptores
-1524 horas
-1027 días
+4430 días
Archivo de publicaciones
🤖🧠 Unleashing the Power of AI with Open Agent Builder: A Visual Workflow Tool for AI Agents
🗓️ 19 Oct 2025
📚 AI News & Trends
In today’s rapidly advancing technological landscape, artificial intelligence (AI) is not just a buzzword, it’s a transformative force across industries. From automating complex tasks to streamlining operations, AI is revolutionizing workflows. However, designing and deploying AI-driven workflows has traditionally required expert-level programming knowledge. Enter Open Agent Builder, a revolutionary tool that democratizes the creation of ...
#AI #ArtificialIntelligence #OpenAgentBuilder #AIAgents #VisualWorkflow #TechInnovation
🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity
🗓️ 18 Oct 2025
📚 AI News & Trends
Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...
#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
Question: What are Python set comprehensions?
Answer:Set comprehensions are similar to list comprehensions but create a set instead of a list. The syntax is:
{expression for item in iterable if condition}
For example, to create a set of squares of even numbers:
squares_set = {x**2 for x in range(10) if x % 2 == 0}{0, 4, 16, 36, 64}
https://t.me/DataScienceQ 🌟
🤖🧠 Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents
🗓️ 17 Oct 2025
📚 AI News & Trends
AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...
#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization
🤖🧠 NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs
🗓️ 17 Oct 2025
📚 AI News & Trends
The rise of large language models (LLMs) has redefined artificial intelligence powering everything from conversational AI to autonomous reasoning systems. However, training these models especially through reinforcement learning (RL) is computationally expensive requiring massive GPU resources and long training cycles. To address this, a team of researchers from NVIDIA, Massachusetts Institute of Technology (MIT), The ...
#QuantumLearning #ReinforcementLearning #LLMs #NVIDIA #MIT #TsinghuaUniversity
Contribute with us to expand the services offered in our channel
We plan to use an advanced AI model to add more information about the most prominent events, models, and articles released and provide explanations.
This requires preparing an infrastructure for our server and purchasing an API for an AI model.
Contribute to the development of our community with us
Contact me @husseinsheikho
🤖🧠 MinerU2.5 by Shanghai AI Lab, Peking University & Shanghai Jiao Tong University Sets New Standard for AI-Powered Document Parsing
🗓️ 15 Oct 2025
📚 AI News & Trends
In the world of digital transformation, the ability to accurately extract and interpret information from complex documents is becoming increasingly essential. Whether for academic research, financial analysis or enterprise automation, document parsing – the process of converting structured and unstructured document data into machine-readable formats plays a vital role. Enter MinerU2.5, a groundbreaking vision-language model ...
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Repost from Machine Learning with Python
☄️ Top 12 YouTube Channels to Learn Python
💐 Python will include 57% of data scientist job ads in 2024 . It is still the number one programming language for data scientists.
✅ Now, if you are looking for the best resources to improve your Python skills, after searching and reviewing various resources, I have prepared a list of 12 top channels that provide first-class Python training, which can turn beginners into professional Python programmers. convert
🎬 Python Programmer channel
┤ 📈 211 videos / 465K SUB
┘ 🔴 Link: Python Programmer
🎬 Luke Barousse channel
┤ 📈 157 videos / 429K SUB
┘ 🔴 Link: Luke Barousse
🎬 codebasics channel
┤ 📈 837 videos / 990K SUB
┘ 🔴 link: codebasics
🎬 StatQuest channel with Josh Starmer
┤ 📈 271 videos / 1.14M SUB
┘ 🔴 Link: StatQuest with Josh Starmer
🎬 Sundas Khalid channel
┤ 📈 143 videos / 203K SUB
┘ 🔴 Link: Sundas Khalid
🎬 Shashank Kalanithi channel
┤ 📈 152 videos / 148K SUB
┘ 🔴 Link: Shashank Kalanithi
🎬 Programming with Mosh channel
┤ 📈 203 videos / 3.85M SUB
┘ 🔴 Link: Programming with Mosh
🎬 Corey Schafer channel
┤ 📈 233 videos / 129K SUB
┘ 🔴 Link: Corey Schafer
🎬 sentdex channel
┤ 📈 1254 videos / 1.3M SUB
┘ 🔴 link: sentdex
🎬 Patrick Loeber channel
┤ 📈 206 videos / 264K SUB
┘ 🔴 Link: Patrick Loeber
🎬 Socratica channel
┤ 📈 659 videos / 876K SUB
┘ 🔴 Link: Socratica
🎬 Tech With Tim channel
┤ 📈 983 videos / 1.48M SUB
┘ 🔴 Link: Tech With Tim
😠 More likes 😠 ➡️ more posts
✈️ http://t.me/codeprogrammer ✅
Question:
What is type hinting in Python, and how does it enhance code quality?
Answer: 👉
