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

India2 913 Education1 493...

📈 Analytical overview of Telegram channel AI and Machine Learning

Channel AI and Machine Learning (@machine_learning_courses) in the English language segment is an active participant. Currently, the community unites 95 477 subscribers, ranking 1 493 in the Education category and 2 913 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 95 477 subscribers.

According to the latest data from 15 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 356 over the last 30 days and by 24 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 9.36%. Within the first 24 hours after publication, content typically collects 2.40% reactions from the total number of subscribers.
Post reach: On average, each post receives 8 940 views. Within the first day, a publication typically gains 2 287 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 13.
Thematic interests: Content is focused on key topics such as learning, llm, linkedin, linux, udemy.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses”

Thanks to the high frequency of updates (latest data received on 16 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

95 477

Subscribers

+2424 hours

+867 days

+35630 days

8 940

Post views

~ 2 28724 hours

~ 3 00648 hours

9.36%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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 svfr

2. Install PyTorch (for your CUDA)

pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2

3. Install dependencies

pip install -r requirements.txt

4. Download models

conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt

5. Start processing videos

python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region

Where task_ids: * 0 — 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/Instructions

95 477

📱Artificial intelligence 📱Responsible AI Framework for Your Enterprise AI Product

95 477

🔅 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

95 477

🔁 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