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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Telegram kanali Machine Learning with Python analitikasi

Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 815 obunachidan iborat bo'lib, Taʼlim toifasida 2 419-o'rinni va Hindiston mintaqasida 5 033-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 67 815 obunachiga ega bo‘ldi.

12 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 57 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 4.13% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.69% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 2 799 marta ko‘riladi; birinchi sutkada odatda 1 149 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 insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Yuqori yangilanish chastotasi (oxirgi ma’lumot 13 Iyun, 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.

67 815
Obunachilar
+324 soatlar
-187 kunlar
+5730 kunlar
Postlar arxiv
Transformer: Multi-Head Attention Math vs Code 👨‍💻 I made this visualization to show you how to implement the multi-head attention math in PyTorch within 50 LoC. Multi-Head Attention is what makes the Transformer's performance outstanding. Is it useful to you❓ 📂 Tags: #ML #TRANSFORMER http://t.me/codeprogrammer ⭐️

Yan Chen - Transformer: Attention by hand ✍️ Is it useful to you❓ 📂 Tags: #ML #TRANSFORMER http://t.me/codeprogrammer ⭐️

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Airdrop in 20/10/2024 Get money from this airdrop and convert $Tomato to usd and subscribe in Coursera grant This is my way to get resources I love airdrops https://t.me/Tomarket_ai_bot/app?startapp=0001oVe7

Airdrop in 20/10/2024 Get money from this airdrop and convert $Tomato to usd and subscribe in Coursera grant This is my way to get resources I love airdrops https://t.me/Tomarket_ai_bot/app?startapp=0001oVe7

Add your name to the list of candidates to win free grants, courses, and books by New Year's. The first 100 names will be selected and awarded grants, courses, or books based on the points they accumulate. Register here 🤠

Pandas ➡️ Polars ➡️ SQL ➡️ PySpark translations: Is it useful to you❓ 📂 Tags: #pandas #Polars #sql #Pyspark http://t.me/code
Pandas ➡️ Polars ➡️ SQL ➡️ PySpark translations: Is it useful to you❓ 📂 Tags: #pandas #Polars #sql #Pyspark http://t.me/codeprogrammer ⭐️

https://t.me/major/start?startapp=418788114 👑 Join me in @Major game and earn $MAJOR token soon! ⭐️ 750 rating bonus for you. ⭐️ 1000 rating bonus if you are Premium.

Transformer by Hand ✍️ in 5 Minutes with Anna Rahn 📂 Tags: #python #ML #Transformer http://t.me/codeprogrammer ⭐️

Web scraping will never be the same! Crawl4AI simplifies web crawling and data extraction, making it ready to use for LLMs an
Web scraping will never be the same! Crawl4AI simplifies web crawling and data extraction, making it ready to use for LLMs and AI applications. Here’s why it’s a game-changer: 🆓 Free and open-source ⭐️ Blazing fast performance, 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown) 🌍 Supports crawling multiple URLs simultaneously 🎨 Extracts all media tags (Images, Audio, Video) 🔗 Extracts all external and internal links But that’s not all: 📚 Extracts metadata from pages ✅ User-agent customization ✅ Takes screenshots of pages 📄 Executes custom JavaScript before crawling 📂 Tags: #python #ML #LLM http://t.me/codeprogrammer ⭐️

Let's open a new page and restore interaction to normal We want 100 interactions and I promise you more

LSTM by hand ✍️ Excel 📂 Tags: #python #ML #LSTM http://t.me/codeprogrammer ⭐️

LSTM by hand ✍️ Excel 📂 Tags: #python #ML #LSTM http://t.me/codeprogrammer ⭐️ Download Excel file 👇

Please enter your name to receive financial funding ranging from $10 to $300, which will allow you to register for Coursera's annual grants. Link for registration and submission.

Hello, my friends, The main reason for making some information and resources paid is that no one appreciates the value of the time and effort we spend to obtain the best results and resources for you. A low number of interactions... a low number of stars. I don't care about the money, but I want recognition for my efforts. I'm not willing to let my hard work and time go to waste for selfish individuals. This is the link to my paid channel where I provide all resources and information for free. https://t.me/+Tdshx2j5cZ00N2Ji Eng. Hussein Sheikho ✅

Based on leaks, Coursera has launched its new token, Circle, which will be converted into real money by the end of next month
Based on leaks, Coursera has launched its new token, Circle, which will be converted into real money by the end of next month. The more points you accumulate, the greater your chances of receiving a long-term Coursera grant. Collect circle tokens from here. Collecting tokens only requires you to complete daily tasks. The number of people working to collect Circle tokens has surpassed five million. Your life will change for the better. Sign up here for the Coursera grant.