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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 67 812 obunachidan iborat bo'lib, Taʼlim toifasida 2 404-o'rinni va Hindiston mintaqasida 5 049-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 67 812 obunachiga ega bo‘ldi.

05 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 77 ga, so‘nggi 24 soatda esa 9 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 2.60% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.50% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 767 marta ko‘riladi; birinchi sutkada odatda 1 695 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 07 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.

67 812
Obunachilar
+924 soatlar
+587 kunlar
+7730 kunlar
Postlar arxiv
This GitHub repository is not a dump of tutorials. Inside, there are 28 production-ready AI projects that can be used. What's
This GitHub repository is not a dump of tutorials. Inside, there are 28 production-ready AI projects that can be used. What's there: Machine learning projects → Airbnb price forecasting → Air ticket cost calculator → Student performance tracker AI for medicine → Chest disease detection → Heart disease prediction → Diabetes risk analysis Generative AI applications → Live chatbot on Gemini → Medical assistant tool → Document analysis tool Computer vision projects → Hand tracking system → Drug recognition app → OpenCV implementations Data analysis dashboards → E-commerce analytics → Restaurant analytics → Cricket statistics tracker And 10 more advanced projects coming soon: → Deepfake detection → Brain tumor classification → Driver drowsiness alert system This is not just a collection of code files. These are end-to-end working applications. View the repository 😲 https://github.com/KalyanM45/AI-Project-Gallery 👉 @codeprogrammer

200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we compl
200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

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200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we compl
200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

Advice on clean code in Python Don't use "naive" datetime without time zones. Store and process time in UTC, and display it t
Advice on clean code in Python Don't use "naive" datetime without time zones. Store and process time in UTC, and display it to the user in his local time zone
import datetime
from zoneinfo import ZoneInfo

# BAD
now = datetime.datetime.now()

print(now.isoformat())
# 2025-10-21T15:03:07.332217

# GOOD
now = datetime.datetime.now(tz=ZoneInfo("UTC"))
print(now.isoformat())
# 2025-10-21T12:04:22.573590+00:00

print(now.astimezone().isoformat())
# 2025-10-21T15:04:22.573590+03:00

I never believed online earning could be this easy until I found this hidden hack. 💸 Imagine making real money without any i
I never believed online earning could be this easy until I found this hidden hack. 💸 Imagine making real money without any investment—sounds unreal? It’s not. Discover how thousands are cashing out DAILY with 50+ proven methods 👉 Start here and join the secret that no one talks about! #ad InsideAds

🚀 Master Data Science & Programming! Unlock your potential with this curated list of Telegram channels. Whether you need boo
🚀 Master Data Science & Programming! Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today! 🔰 Machine Learning with Python Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. https://t.me/CodeProgrammer 🔖 Machine Learning Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications. https://t.me/DataScienceM 🧠 Code With Python This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills. https://t.me/DataScience4 🎯 PyData Careers | Quiz Python Data Science jobs, interview tips, and career insights for aspiring professionals. https://t.me/DataScienceQ 💾 Kaggle Data Hub Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects. https://t.me/datasets1 🧑‍🎓 Udemy Coupons | Courses The first channel in Telegram that offers free Udemy coupons https://t.me/DataScienceC 😀 ML Research Hub Advancing research in Machine Learning – practical insights, tools, and techniques for researchers. https://t.me/DataScienceT 💬 Data Science Chat An active community group for discussing data challenges and networking with peers. https://t.me/DataScience9 🐍 Python Arab| بايثون عربي The largest Arabic-speaking group for Python developers to share knowledge and help. https://t.me/PythonArab 🖊 Data Science Jupyter Notebooks Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post. https://t.me/DataScienceN 📺 Free Online Courses | Videos Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners. https://t.me/DataScienceV 📈 Data Analytics Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. https://t.me/DataAnalyticsX 🎧 Learn Python Hub Master Python with step-by-step courses – from basics to advanced projects and practical applications. https://t.me/Python53 ⭐️ Research Papers Professional Academic Writing & Simulation Services https://t.me/DataScienceY ━━━━━━━━━━━━━━━━━━ Admin: @HusseinSheikho

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In scientific work, the most time is spent on reading articles, data, and reports. On GitHub, there is a collection called Aw
In scientific work, the most time is spent on reading articles, data, and reports. On GitHub, there is a collection called Awesome AI for Science -»»» a catalog of AI tools for all stages of research. Inside: -» working with literature -» data analysis -» turning articles into posters -» automating experiments -» tools for biology, chemistry, physics, and other fields GitHub: http://github.com/ai-boost/awesome-ai-for-science The list includes Paper2Poster, MinerU, The AI Scientist, as well as articles, datasets, and frameworks. In fact, this is a complete set of tools for AI support in scientific research. 👉 https://t.me/CodeProgrammer

Automatic translator in Python! We translate a text in a few lines using deep-translator. It supports dozens of languages: from English and Russian to Japanese and Arabic. Install the library:
pip install deep-translator
Example of use:
from deep_translator import GoogleTranslator

text = "Hello, how are you?"
result = GoogleTranslator(source="ru", target="en").translate(text)

print("Original:", text)
print("Translation:", result)
Mass translation of a list:
texts = ["Hello", "What's your name?", "See you later"]
for t in texts:
    print("→", GoogleTranslator(source="ru", target="es").translate(t))
🔥 We get a mini-Google Translate right in Python: you can embed it in a chatbot, use it in notes, or automate work with the API. 🚪 @DataScience4

🚀Stanford just completed a must-watch for anyone serious about AI: 🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲
🚀Stanford just completed a must-watch for anyone serious about AI: 🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold. If you’re building your AI career, stop scrolling. This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum. 📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & MoEs • LLM finetuning (LoRA, RLHF, supervised) • Evaluation techniques (LLM-as-a-judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling • Agentic workflows (RAG, tool calling) 🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention. 🎥 Watch these now: - Lecture 1: https://lnkd.in/dDER-qyp - Lecture 2: https://lnkd.in/dk-tGUDm - Lecture 3: https://lnkd.in/drAPdjJY - Lecture 4: https://lnkd.in/e_RSgMz7 - Lecture 5: https://lnkd.in/eivMA9pe - Lecture 6: https://lnkd.in/eYwwwMXn - Lecture 7: https://lnkd.in/eKwkEDXV - Lecture 8: https://lnkd.in/eEWvyfyK - Lecture 9: https://lnkd.in/euiKRGaQ 🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them. If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss. Let’s level up. https://t.me/CodeProgrammer 😅

🚀Stanford just completed a must-watch for anyone serious about AI: 🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲
🚀Stanford just completed a must-watch for anyone serious about AI: 🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold. If you’re building your AI career, stop scrolling. This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum. 📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & MoEs • LLM finetuning (LoRA, RLHF, supervised) • Evaluation techniques (LLM-as-a-judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling • Agentic workflows (RAG, tool calling) 🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention. 🎥 Watch these now: - Lecture 1: https://lnkd.in/dDER-qyp - Lecture 2: https://lnkd.in/dk-tGUDm - Lecture 3: https://lnkd.in/drAPdjJY - Lecture 4: https://lnkd.in/e_RSgMz7 - Lecture 5: https://lnkd.in/eivMA9pe - Lecture 6: https://lnkd.in/eYwwwMXn - Lecture 7: https://lnkd.in/eKwkEDXV - Lecture 8: https://lnkd.in/eEWvyfyK - Lecture 9: https://lnkd.in/euiKRGaQ 🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them. If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss. Let’s level up. https://t.me/CodeProgrammer 😅

Просто зацените: парень показывает внутрянку крупных брендов, как компании вечно водят вас за нос и заставляют тратить деньги на безделушки и Лабубу Ничего не продает, просто куча трушных постов про маркетинг и конечно же мемы (а куда без них). Читайте: @maratyus

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These questions are taken from the book "Python Workout 2025"

Repost from ADMINOTEKA
Админотека — это лучший сервис для монетизации твоего канала! Привет, давай знакомиться. Не самое скромное приветствие получи
Админотека — это лучший сервис для монетизации твоего канала! Привет, давай знакомиться. Не самое скромное приветствие получилось, но мы можем подтвердить свои слова. 🌑 Ведь у нас зарабатывают даже самые маленькие каналы с 20 охватами 🌑 Стабильные офферы каждую неделю 🌑 Алгоритмы, рейтинг, защита сделок — и всё это автоматизировано 🌑 Удобные выплаты на привязанный кошелек 🌑 Здесь же можно и купить размещения по самому низкому прайсу на рынке 💸 Подключай свой канал и проверяй на практике. Преврати свой канал в реальный доход вместе с нами!

1. What will be the output of the following code?
def add_item(item, lst=None):
    if lst is None:
        lst = []
    lst.append(item)
    return lst

print(add_item(1))
print(add_item(2))
A. [1] then [2] B. [1] then [1, 2] C. [] then [] D. Raises TypeError Correct answer: A. 2. What is printed by this code?
x = 10
def func():
    print(x)
    x = 5

func()
A. 10 B. 5 C. None D. UnboundLocalError Correct answer: D. 3. What is the result of executing this code?
a = [1, 2, 3]
b = a[:]
a.append(4)
print(b)
A. [1, 2, 3, 4] B. [4] C. [1, 2, 3] D. [] Correct answer: C. 4. What does the following expression evaluate to?
bool("False")
A. False B. True C. Raises ValueError D. None Correct answer: B. 5. What will be the output?
print(type({}))
A. <class 'list'> B. <class 'set'> C. <class 'dict'> D. <class 'tuple'> Correct answer: C. 6. What is printed by this code?
x = (1, 2, [3])
x[2] += [4]
print(x)
A. (1, 2, [3]) B. (1, 2, [3, 4]) C. TypeError D. AttributeError Correct answer: C. 7. What does this code output?
print([i for i in range(3) if i])
A. [0, 1, 2] B. [1, 2] C. [0] D. [] Correct answer: B. 8. What will be printed?
d = {"a": 1}
print(d.get("b", 2))
A. None B. KeyError C. 2 D. "b" Correct answer: C. 9. What is the output?
print(1 in [1, 2], 1 is 1)
A. True True B. True False C. False True D. False False Correct answer: A. 10. What does this code produce?
def gen():
    for i in range(2):
        yield i

g = gen()
print(next(g), next(g))
A. 0 1 B. 1 2 C. 0 0 D. StopIteration Correct answer: A. 11. What is printed?
print({x: x*x for x in range(2)})
A. {0, 1} B. {0: 0, 1: 1} C. [(0,0),(1,1)] D. Error Correct answer: B. 12. What is the result of this comparison?
print([] == [], [] is [])
A. True True B. False False C. True False D. False True Correct answer: C. 13. What will be printed?
def f():
    try:
        return "A"
    finally:
        print("B")

print(f())
A. A B. B C. B then A D. A then B Correct answer: C. 14. What does this code output?
x = [1, 2]
y = x
x = x + [3]
print(y)
A. [1, 2, 3] B. [3] C. [1, 2] D. Error Correct answer: C. 15. What is printed?
print(type(i for i in range(3)))
A. <class 'list'> B. <class 'tuple'> C. <class 'generator'> D. <class 'range'> Correct answer: C.

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Repost from Machine Learning
100+ LLM Interview Questions and Answers (GitHub Repo) Anyone preparing for #AI/#ML Interviews, it is mandatory to have good
100+ LLM Interview Questions and Answers (GitHub Repo) Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics. This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like LLM Inference LLM Fine-Tuning LLM Architectures LLM Pretraining Prompt Engineering etc. 🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub https://t.me/DataScienceM