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AI and Machine Learning
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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Mostrar más📈 Análisis del canal de Telegram AI and Machine Learning
El canal AI and Machine Learning (@machine_learning_courses) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 93 946 suscriptores, ocupando la posición 1 568 en la categoría Educación y el puesto 3 028 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 93 946 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 993, y en las últimas 24 horas de 92, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.92%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.62% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 7 435 visualizaciones. En el primer día suele acumular 1 526 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 learning, llm, linkedin, linux, udemy.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
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 Educación.
93 946
Suscriptores
+9224 horas
+1097 días
+99330 días
Archivo de publicaciones
94 001
🔅 Deep Learning with Python: Foundations
📝 Discover the fundamental concepts and techniques required to implement basic deep learning models using Python.
🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 1h 54m
📋 Topics: Deep Learning, Python
🔗 Join Artificial intelligence for more courses
94 001
🔅 PREMIUM CHANNELS
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🔰 Web Development
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220k| 🔰 Linkedin Learning
143k| 🔰 Udemy Premium
134k| 🔰 Web Development
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121k| 🔰 Python 3
099k| 🔰 JavaScript Training
091k| 🔰 Machine Learning
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071k| 🔰 Data Analysis and Databases
069k| 🔰 Artificial Intelligence
064k| 🔰 Linux and DevOps
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064k| 🔰 React and NextJs
050k| 🔰 100 Days of Python
049k| 🔰 OpenAI Mastery
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049k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
042k| 🔰 Udemy Learning
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040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
036k| 🔰 Linkedin Learning Courses
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035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
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031k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
023k| 🔰 The Coding Space
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023k| 🔰 Linux Training
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🔰 Add Your Channel
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🔰 2hrs on top & 8hrs in channel!
94 001
🔅 Deep Learning with Python: Convolutional Neural Networks
📝 Gain hands-on experience building, training, and evaluating convolutional neural networks (CNNs) using Python for image classification, object detection, and segmentation.
🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 1h 34m
📋 Topics: Convolutional Neural Networks, Deep Learning, Python
🔗 Join Artificial intelligence for more courses
94 001
🔅 PREMIUM CHANNELS
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
220k| 🔰 Linkedin Learning
143k| 🔰 Udemy Premium
134k| 🔰 Web Development
-◦-◦--◦-
121k| 🔰 Python 3
099k| 🔰 JavaScript Training
091k| 🔰 Machine Learning
-◦-◦--◦-
071k| 🔰 Data Analysis and Databases
069k| 🔰 Artificial Intelligence
064k| 🔰 Linux and DevOps
-◦-◦--◦-
064k| 🔰 React and NextJs
050k| 🔰 100 Days of Python
049k| 🔰 OpenAI Mastery
-◦-◦--◦-
049k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
042k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
036k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
-◦-◦--◦-
031k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
023k| 🔰 The Coding Space
-◦-◦--◦-
023k| 🔰 Linux Training
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 2hrs on top & 8hrs in channel!
94 001
AI Outperforms Average Human in Some Creativity Tests
Recent research shows that advanced AI models are now scoring above the average human on certain standard tests of creative thinking — including idea generation and problem-solving tasks.
While these benchmarks don’t capture the full range of human creativity, the results suggest AI is making measurable progress in areas once seen as uniquely human.
The findings are part of ongoing work to understand how AI can assist in creative workflows, not just automate routine tasks.
94 001
AI + Crypto is entering a new phase: confidential compute at scale.
AlphaTON Capital (Nasdaq: $ATON) builds a vertically integrated, full-stack AI infrastructure inside the Telegram ecosystem: from NVIDIA B200/B300 GPU hardware to middleware, validators, and native Telegram apps.
We’re growing our English-speaking community to share insights on:
• Telegram-native rails & ecosystem growth
• Key partners to pay attention to
• Privacy-preserving AI infrastructure
• GPU deployment & scaling
• Institutional infrastructure strategy
If you’re tracking the convergence of AI, crypto, and Telegram’s billion-user distribution, join the conversation.
Follow the expert builders.
🔗 Join the community.
94 001
Introducing Vanna: An Open-Source Text-to-SQL Tool | Daily Dose of Data Science posted on the topic | LinkedIn
Finally! A Text-to-SQL tool that actually works!
(100% open-source, 20k+ stars)
Vanna is an open-source RAG framework for complex Text-to-SQL generation, designed for handling dynamic datasets.
Works in 2 easy steps:
1️⃣ Train a RAG “model” on your data.
2️⃣ Ask questions in natural language which will return SQL queries that can be set up to automatically run on your database.
Key features:
🎯 High accuracy on complex datasets
🤖 Self-learning: improves with each query
🔒 Secure: data never leaves your environment
🌐 Connect to any SQL DB (Snowflake, Redshift, etc.)
🧩 Multiple front-end integrations (Jupyter, Slack, etc.)
🌐 Vanna GitHub: https://github.com/vanna-ai/vanna
94 001
Python Developer (Twitter)
12 Frameworks to Build MCP AI Agents
MCP (Model Context Protocol) enables AI agents to interact with tools, memory, and APIs via structured formats. Here are some frameworks that help developers build such agents are as follows:
1 - Open AI SDK: Enables building agentic AI apps with built-in support for MCP.
2 - Composio: SDK to integrate OpenAI agents with Composio-managed MCP-compatible servers and workflows.
3 - MCP Python SDK: Official Python SDK to implement servers that conform to the MCP specification.
4 - LastMile MCP Agent: A workflow-driven framework for creating MCP-compliant agents with task coordination logic.
5 - MCP TypeScript SDK: TypeScript toolkit to build MCP-compatible servers based on the official schema.
6 - Google ADK: Google’s open-source Agent Development Kit with native support for MCP servers.
7 - Langchain MCP Adapter: A lightweight wrapper that connects LangChain/LangGraph with MCP-based toolchains.
8 - CopilotKit MCP Support:...
View original post
94 001
Resonant is a mini-app that connects your decision patterns to your AI Agents. Generate your personal Agentic Memory Card now!
https://t.me/ResonantAlphaBot/resonant?startapp
94 001
🔅 Deep Learning: Image Recognition
📝 Learn how to design, build, and deploy a deep neural network to serve as an image recognition system.
🌐 Author: Isil Berkun
🔰 Level: Intermediate
⏰ Duration: 2h 14m
📋 Topics: Image Recognition, Deep Learning, Computer Vision
🔗 Join Artificial intelligence for more courses
94 001
🧠 AI isn’t a single switch you flip.
It is a sophisticated stack of overlapping technologies that has evolved over seven decades.
Understanding this hierarchy is the difference between chasing hype and building a scalable enterprise strategy.
The AI Stack:
1950s: Artificial Intelligence (The Foundation)
1980s: Machine Learning (The Engine)
2010s: Deep Learning (The Scale)
2020s: Generative AI (The Innovation)
2025+: Agentic AI (The Frontier)
We are currently witnessing the most significant shift yet: the transition from AI as an assistant to AI as an orchestrator.
Capgemini’s 2025 Agentic AI report finds 37% of organizations now piloting (23%) or scaling (14%) AI agents, marking the shift from assistants to orchestration.
These systems don’t just “chat.” They plan and execute multi-step workflows independently.
Enterprises will deploy autonomous agents from 2025 as tools transition from assistants to orchestration systems.
The goal is no longer just processing information. It is autonomous action.
94 001
Not all Al is the same, and understanding the differences is becoming essential.
Traditional Al focuses on prediction, classification, and anomaly detection using historical data.
Generative Al creates content like text, code, images, and summaries from prompts. Agentic Al goes a step further by taking action, using tools, maintaining context, orchestrating workflows, and executing complex tasks with minimal human input.
As Al evolves from automation to autonomy, businesses gain speed, efficiency, and smarter decision-making. ai is no longer just about generating answers; it’s about getting real work done.
94 001
Artificial intelligence is not a single technology but a layered system where each level builds on the previous one. It starts with AI as the broad concept, moves into machine learning that learns from data, neural networks inspired by the human brain, and deep learning that powers vision, speech, and language.
On top of that comes generative AI, capable of creating text, images, and media, and finally agentic AI, which can reason, use tools, and act autonomously toward goals. Understanding these layers helps make sense of how modern AI systems work and where the future of intelligent technology is headed.
