AI and Machine Learning

Kanalga Telegram’da o‘tish

Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

Ko'proq ko'rsatish

Hindiston2 913 Taʼlim1 493...

📈 Telegram kanali AI and Machine Learning analitikasi

AI and Machine Learning (@machine_learning_courses) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 95 477 obunachidan iborat bo'lib, Taʼlim toifasida 1 493-o'rinni va Hindiston mintaqasida 2 913-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 95 477 obunachiga ega bo‘ldi.

15 Sentabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 356 ga, so‘nggi 24 soatda esa 24 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 9.36% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.40% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 8 940 marta ko‘riladi; birinchi sutkada odatda 2 287 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 13 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent learning, llm, linkedin, linux, udemy kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 16 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

95 477

Obunachilar

+2424 soatlar

+867 kun

+35630 kun

8 940

Post ko'rishlar

~ 2 28724 soatlar

~ 3 00648 soatlar

9.36%

Muloqot nisbati

~ 1

Kuniga postlar

Ads index

beta

Ma'lumot yuklanmoqda...

O'xshash kanallar

143.9K

Zero To Mastery

41.9K

Best Telegram Channels

14.8K

LinkedIn Learning (Lynda)

4.5K

Artificial Intelligence / Deep & Machine Learning (Data Science)

2.6K

Best Offers - Courses, Books etc

Ko'proq kanallar

Taglar buluti

Kirish va chiqish esdaliklari

---

Obunachilarni jalb qilish

Sentabr '26

+302

0 kanalda

Avgust '26

+527

2 kanalda

Get PRO

Iyul '26

+780

16 kanalda

Get PRO

Iyun '26

+1 055

1 kanalda

Get PRO

May '26

+1 165

4 kanalda

Get PRO

Aprel '26

+792

20 kanalda

Get PRO

Mart '26

+821

19 kanalda

Get PRO

Fevral '26

+1 063

18 kanalda

Get PRO

Yanvar '26

+1 309

18 kanalda

Get PRO

Dekabr '25

+1 139

19 kanalda

Get PRO

Noyabr '25

+1 241

20 kanalda

Get PRO

Oktabr '25

+1 504

18 kanalda

Get PRO

Sentabr '25

+2 028

18 kanalda

Get PRO

Avgust '25

+2 762

19 kanalda

Get PRO

Iyul '25

+2 186

25 kanalda

Get PRO

Iyun '25

+1 398

17 kanalda

Get PRO

May '25

+2 047

22 kanalda

Get PRO

Aprel '25

+1 711

24 kanalda

Get PRO

Mart '25

+2 847

23 kanalda

Get PRO

Fevral '25

+3 008

22 kanalda

Get PRO

Yanvar '25

+3 423

0 kanalda

Get PRO

Dekabr '24

+4 022

4 kanalda

Get PRO

Noyabr '24

+5 156

2 kanalda

Get PRO

Oktabr '24

+2 670

21 kanalda

Get PRO

Sentabr '24

+3 586

23 kanalda

Get PRO

Avgust '24

+3 742

21 kanalda

Get PRO

Iyul '24

+4 400

25 kanalda

Get PRO

Iyun '24

+4 554

25 kanalda

Get PRO

May '24

+6 333

15 kanalda

Get PRO

Aprel '24

+1 997

1 kanalda

Get PRO

Mart '24

+2 668

2 kanalda

Get PRO

Fevral '24

+1 950

0 kanalda

Get PRO

Yanvar '24

+2 006

3 kanalda

Get PRO

Dekabr '23

+1 319

2 kanalda

Get PRO

Noyabr '23

+384

0 kanalda

Get PRO

Oktabr '23

+381

0 kanalda

Get PRO

Sentabr '23

+583

0 kanalda

Get PRO

Avgust '23

+1 318

0 kanalda

Get PRO

Iyul '23

+1 445

0 kanalda

Get PRO

Iyun '23

+1 200

0 kanalda

Get PRO

May '23

+1 576

0 kanalda

Get PRO

Aprel '23

+1 012

0 kanalda

Get PRO

Mart '23

+944

0 kanalda

Get PRO

Fevral '23

+918

0 kanalda

Get PRO

Yanvar '23

+1 089

0 kanalda

Get PRO

Dekabr '22

+944

0 kanalda

Get PRO

Noyabr '22

+789

0 kanalda

Get PRO

Oktabr '22

+581

0 kanalda

Get PRO

Sentabr '22

+885

0 kanalda

Get PRO

Avgust '22

+826

0 kanalda

Get PRO

Iyul '22

+844

0 kanalda

Get PRO

Iyun '22

+804

0 kanalda

Get PRO

May '22

+780

0 kanalda

Get PRO

Aprel '22

+4 074

0 kanalda

Get PRO

Mart '220

0 kanalda

Get PRO

Fevral '220

0 kanalda

Get PRO

Yanvar '220

0 kanalda

Get PRO

Dekabr '210

0 kanalda

Get PRO

Noyabr '21

+231

0 kanalda

Get PRO

Oktabr '21

+145

0 kanalda

Get PRO

Sentabr '21

+160

0 kanalda

Get PRO

Avgust '21

+143

0 kanalda

Get PRO

Iyul '21

+139

0 kanalda

Get PRO

Iyun '21

+359

0 kanalda

Get PRO

May '21

+513

0 kanalda

Sana	Obunachilarni jalb qilish	Esdaliklar	Kanallar
16 Sentabr	+5
15 Sentabr	+28
14 Sentabr	+11
13 Sentabr	+8
12 Sentabr	+10
11 Sentabr	+13
10 Sentabr	+20
09 Sentabr	+17
08 Sentabr	+17
07 Sentabr	+26
06 Sentabr	+22
05 Sentabr	+17
04 Sentabr	+45
03 Sentabr	+33
02 Sentabr	+24
01 Sentabr	+6

Kanal postlari

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

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	+4 n8n roadmap	12 136
16	+5 100 AI ML projects for all levels	11 481
17	+5 100 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	+1 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	8 899

Barcha postlarni ko‘rish