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Machine Learning with Python

Machine Learning with Python

Kanalga Telegram’da o‘tish

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

Ko'proq ko'rsatish

📈 Telegram kanali Machine Learning with Python analitikasi

Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 826 obunachidan iborat bo'lib, Taʼlim toifasida 2 419-o'rinni va Hindiston mintaqasida 4 901-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 67 826 obunachiga ega bo‘ldi.

23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 107 ga, so‘nggi 24 soatda esa 23 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 4.73% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.30% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 3 210 marta ko‘riladi; birinchi sutkada odatda 884 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 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 24 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.

67 826
Obunachilar
+2324 soatlar
-367 kunlar
+10730 kunlar
Postlar arxiv
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Thanks 🙏

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Curated list of distributed systems papers and books 📚📖 https://github.com/theanalyst/awesome-distributed-systems ✨ Join Be
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼 The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯 It's more reliable to gather everything into one expression:
s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
One mask, one point of truth. ✅ It's easier to debug. Fewer surprises when the code grows. 🚀 #Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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