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
Больше📈 Аналитический обзор Telegram-канала AI and Machine Learning
Канал AI and Machine Learning (@machine_learning_courses) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 95 477 подписчиков, занимая 1 493 место в категории Образование и 2 913 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 95 477 подписчиков.
Согласно последним данным от 15 сентября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 356, а за последние 24 часа — 24, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 9.36%. В первые 24 часа после публикации контент обычно набирает 2.40% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 8 940 просмотров. В течение первых суток публикация набирает 2 287 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 13.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, llm, linkedin, linux, udemy.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
Благодаря высокой частоте обновлений (последние данные получены 16 сентября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
95 477
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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.
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📌 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
