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 95 477 suscriptores, ocupando la posición 1 493 en la categoría Educación y el puesto 2 913 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 95 477 suscriptores.
Según los últimos datos del 15 septiembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 356, y en las últimas 24 horas de 24, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 9.36%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.40% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 8 940 visualizaciones. En el primer día suele acumular 2 287 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 13.
- 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 16 septiembre, 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.
95 477
Suscriptores
+2424 horas
+867 días
+35630 días
Archivo de publicaciones
95 477
Colorizing old black-and-white videos and "bringing faces to life" for FREE
SVFR — a full-fledged framework for restoring faces in videos.
It can:
💬 BFR — improve blurry faces.
💬 Colorization — colorize black-and-white videos.
💬 Inpainting — redraw damaged areas.
💬 and combine all of this in one pass.
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
⚙️ Installation locally:
1. Create an environment
conda create -n svfr python=3.9 -y
conda activate svfrpip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2pip install -r requirements.txtconda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xtpython infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region0 — face enhancement
* 1 — colorization
* 2 — redrawing damage
An ideal tool if:
🟢you're restoring archival videos;
🟢you're creating historical content;
🟢you're working with neural networks and video effects;
🟢you want a wow result without paid services.
▶️ Demo on Hugging Face
🌐 GitHub/Instructions95 477
🔅 Responsible AI Framework for Your Enterprise AI Product
📝 Master responsible AI with the Five Rings framework. Learn how to build ethical, secure, and transparent AI products that align with human values and business success.
🌐 Author: Alina Zhang
🔰 Level: Intermediate
⏰ Duration: 45m
📋 Topics: Responsible AI, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
95 477
Machine Learning in Python (Course Notes)
I just went through an amazing resource on MachineLearning in Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
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🔁 K-Fold Cross Validation
K-Fold exists to answer one honest question:
Will this model work on unseen data?
A single train/test split is unreliable, especially with small datasets.
So K-Fold simulates multiple “future tests” using the same data.
🧠 What It Really Does
Instead of one split, we:
🔀 Divide data into K folds
🔁 Train the model K times
📦 Each time: one fold validates, the rest train
📊 Average the scores
Every sample gets validated once, which reduces evaluation noise and gives a more trustworthy estimate.
Important: It improves evaluation, not the model itself.
⚠️ What People Often Miss
🚫 Do NOT use K-Fold as your final test. Keep a separate test set
⚖️ Use Stratified K-Fold for imbalanced classification.
⏳ Do NOT use standard K-Fold for time series.
📊 K = 5 or 10 is usually enough.
✅ In short K-Fold is just:
A smart way to reuse limited data to simulate multiple real-world tests.
No magic. Just careful evaluation.
95 477
📌 A comprehensive masterclass on Claude Code is available via this repository: https://github.com/luongnv89/claude-howto.
This resource provides a detailed visual and practical guide for one of the most powerful tools for developers. The repository includes:
• Step-by-step learning paths covering basic commands (/init, /plan) to advanced features such as MCP, hooks, and agents, achievable in approximately 11–13 hours. 📚
• An extensive library of custom commands designed for real-world tasks.
• Ready-made memory templates for both individual and team workflows.
• Instructions and scripts for:
- Automated code review.
- Style and standards compliance checks.
- API documentation generation.
• Automation cycles enabling autonomous operation of Claude without direct user intervention. ⚙️
• Integration with external tools, including GitHub and various APIs, presented with step-by-step guidance.
• Diagrams and charts to facilitate understanding, suitable for beginners. 📊
• Examples for configuring highly specialized sub-agents.
• Dedicated learning scripts, such as tools for generating educational books and materials to master specific topics efficiently.
Access the full guide here: https://github.com/luongnv89/claude-howto
95 477
🔅 Deep Learning with TensorFlow: Insights and Innovations
📝 Explore the evolving world of deep learning with TensorFlow, including the basics of generative AI, with practical, hands-on examples.
🌐 Author: Isil Berkun
🔰 Level: Intermediate
⏰ Duration: 3h 6m
📋 Topics: TensorFlow, Deep Learning, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
95 477
🔅 A Content Marketer's Guide to Responsible AI
📝 Learn to use AI responsibly in content marketing, balancing personalization, privacy, and ethical AI practices.
🌐 Author: Lauren Diethelm
🔰 Level: General
⏰ Duration: 23m
📋 Topics: Content Marketing, Artificial Intelligence for Business
🔗 Join Artificial intelligence for more courses
95 477
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AI Agents vs Agentic AI... what’s the actual difference?
There are 3 types of AI workflows worth knowing and each performs a different task. If you don’t understand these you’re probably falling behind.Non-Agentic AI: Basic prompt-response AI with no memory/reasoning. They’re fast, cheap, and universally accessible, requires no technical build or integration and great for clear, one-off tasks. Agentic AI: Self-managing AI system that can plan and execute. Great for handling complex, changing projects. They can integrate with tools and databases and produce more reliable outcomes. AI Agent: A single-task AI worker designed to automate one task. Automates repetitive, time-consuming tasks, quick setup and cost-efficient and easy to test and refine within roles In short: AI Agents = Single-task automation Agentic AI = Multi-step problem solving
