Machine Learning
Kanalga Telegram’da o‘tish
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Machine Learning analitikasi
Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 41 668 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 145-o'rinni va Suriya mintaqasida 215-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 41 668 obunachiga ega bo‘ldi.
05 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 533 ga, so‘nggi 24 soatda esa 13 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 6.25% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.92% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 601 marta ko‘riladi; birinchi sutkada odatda 800 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 6 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 06 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
41 668
Obunachilar
+1324 soatlar
+1357 kun
+53330 kun
Postlar arxiv
41 668
Repost from Machine Learning with Python
🎓 $10,000 Scholarship Grant — Yours for the Taking! 🎓
Imagine this: $10,000 handed to you — completely FREE — to fund your education. No essays. No essays. No application fees. Just your effort inside our bot. 💸
🏆 Reach 10,000 points and the scholarship is yours.
🎁 Plus, you unlock a lifetime subscription to all our paid courses & books — yours forever.
Here's how easy it is to earn points:
🔗 Invite friends with your referral link → +50 points each
📺 Watch ads → +15 points per ad
🧠 Answer the Question of the Day → +15 points
Every small action gets you closer to that $10,000. Every friend you invite doubles your chance. Every question you answer sharpens your mind AND your wallet.
🚀 Don't wait. Start now:
👉 https://t.me/UdemySybot?start=ref_148350890
The next scholarship winner could be you. All it takes is 10,000 points — and the discipline to start today.
🔥 Your future self will thank you. 🔥
41 668
"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent"
To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation.
It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently.
https://algebrica.org/learning-mathematics/
41 668
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
41 668
Repost from Machine Learning with Python
+1
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://t.me/CodeProgrammer 🤩
41 668
Repost from Machine Learning with Python
If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://t.me/CodeProgrammer
41 668
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
41 668
🤖 A Practical Tip for ML Data Collection
When building a machine learning project, getting enough useful data is often just as important as the model itself.
If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned.
A residential proxy can help with this by routing your requests through IPs from different regions.
For example, with Python:
import requests
proxies = {
"http": "http://USER:PASSWORD@HOST:PORT",
"https": "http://USER:PASSWORD@HOST:PORT"
}
response = requests.get(
"https://example.com",
proxies=proxies
)
print(response.status_code)