AI and Machine Learning
Open in Telegram
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Show more📈 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
Data loading in progress...
Similar Channels
Tags Cloud
Incoming and Outgoing Mentions
---
---
---
---
---
---
Attracting Subscribers
September '26
September '26
+302
in 0 channels
August '26
+527
in 2 channels
Get PRO
July '26
+780
in 16 channels
Get PRO
June '26
+1 055
in 1 channels
Get PRO
May '26
+1 165
in 4 channels
Get PRO
April '26
+792
in 20 channels
Get PRO
March '26
+821
in 19 channels
Get PRO
February '26
+1 063
in 18 channels
Get PRO
January '26
+1 309
in 18 channels
Get PRO
December '25
+1 139
in 19 channels
Get PRO
November '25
+1 241
in 20 channels
Get PRO
October '25
+1 504
in 18 channels
Get PRO
September '25
+2 028
in 18 channels
Get PRO
August '25
+2 762
in 19 channels
Get PRO
July '25
+2 186
in 25 channels
Get PRO
June '25
+1 398
in 17 channels
Get PRO
May '25
+2 047
in 22 channels
Get PRO
April '25
+1 711
in 24 channels
Get PRO
March '25
+2 847
in 23 channels
Get PRO
February '25
+3 008
in 22 channels
Get PRO
January '25
+3 423
in 0 channels
Get PRO
December '24
+4 022
in 4 channels
Get PRO
November '24
+5 156
in 2 channels
Get PRO
October '24
+2 670
in 21 channels
Get PRO
September '24
+3 586
in 23 channels
Get PRO
August '24
+3 742
in 21 channels
Get PRO
July '24
+4 400
in 25 channels
Get PRO
June '24
+4 554
in 25 channels
Get PRO
May '24
+6 333
in 15 channels
Get PRO
April '24
+1 997
in 1 channels
Get PRO
March '24
+2 668
in 2 channels
Get PRO
February '24
+1 950
in 0 channels
Get PRO
January '24
+2 006
in 3 channels
Get PRO
December '23
+1 319
in 2 channels
Get PRO
November '23
+384
in 0 channels
Get PRO
October '23
+381
in 0 channels
Get PRO
September '23
+583
in 0 channels
Get PRO
August '23
+1 318
in 0 channels
Get PRO
July '23
+1 445
in 0 channels
Get PRO
June '23
+1 200
in 0 channels
Get PRO
May '23
+1 576
in 0 channels
Get PRO
April '23
+1 012
in 0 channels
Get PRO
March '23
+944
in 0 channels
Get PRO
February '23
+918
in 0 channels
Get PRO
January '23
+1 089
in 0 channels
Get PRO
December '22
+944
in 0 channels
Get PRO
November '22
+789
in 0 channels
Get PRO
October '22
+581
in 0 channels
Get PRO
September '22
+885
in 0 channels
Get PRO
August '22
+826
in 0 channels
Get PRO
July '22
+844
in 0 channels
Get PRO
June '22
+804
in 0 channels
Get PRO
May '22
+780
in 0 channels
Get PRO
April '22
+4 074
in 0 channels
Get PRO
March '220
in 0 channels
Get PRO
February '220
in 0 channels
Get PRO
January '220
in 0 channels
Get PRO
December '210
in 0 channels
Get PRO
November '21
+231
in 0 channels
Get PRO
October '21
+145
in 0 channels
Get PRO
September '21
+160
in 0 channels
Get PRO
August '21
+143
in 0 channels
Get PRO
July '21
+139
in 0 channels
Get PRO
June '21
+359
in 0 channels
Get PRO
May '21
+513
in 0 channels
| Date | Subscriber Growth | Mentions | Channels | |
| 16 September | +5 | |||
| 15 September | +28 | |||
| 14 September | +11 | |||
| 13 September | +8 | |||
| 12 September | +10 | |||
| 11 September | +13 | |||
| 10 September | +20 | |||
| 09 September | +17 | |||
| 08 September | +17 | |||
| 07 September | +26 | |||
| 06 September | +22 | |||
| 05 September | +17 | |||
| 04 September | +45 | |||
| 03 September | +33 | |||
| 02 September | +24 | |||
| 01 September | +6 |
Channel Posts
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/Instructions| 2 | 📱Artificial intelligence
📱Responsible AI Framework for Your Enterprise AI Product | 4 481 |
| 3 |  🔅 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 | 4 408 |
| 4 | 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 | 6 129 |
| 5 |  🔁 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. | 7 155 |
| 6 |  📌 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 | 8 235 |
| 7 | 📱Artificial intelligence
📱Deep Learning with TensorFlow: Insights and Innovations | 8 635 |
| 8 | 📱Artificial intelligence
📱Deep Learning with TensorFlow: Insights and Innovations | 8 272 |
| 9 | 📱Artificial intelligence
📱Deep Learning with TensorFlow: Insights and Innovations | 8 182 |
| 10 | 📱Artificial intelligence
📱Deep Learning with TensorFlow: Insights and Innovations | 8 261 |
| 11 | 📱Artificial intelligence
📱Deep Learning with TensorFlow: Insights and Innovations | 8 170 |
| 12 | 📱Artificial intelligence
📱Deep Learning with TensorFlow: Insights and Innovations | 8 242 |
| 13 |  🔅 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 | 8 367 |
| 14 |  🧠 The World of AI | 10 525 |
| 15 |  +4n8n roadmap | 12 136 |
| 16 |  +5100 AI ML projects for all levels | 11 481 |
| 17 |  +5100 AI ML projects for all levels | 11 152 |
| 18 | 📱Artificial intelligence
📱A Content Marketer's Guide to Responsible AI | 8 637 |
| 19 |  🔅 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 | 7 906 |
| 20 |  +1AI 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 | 8 899 |
