Machine Learning with Python – Telegram
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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@codeprogrammer
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Forwarded from Machine Learning
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://news.1rj.ru/str/DataScienceM
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I'm happy to announce that freeCodeCamp has launched a new certification in #Python 🐍

» Learning the basics of programming
» Project development
» Final exam
» Obtaining a certificate

Everything takes place directly in the browser, without installation. This is one of the six certificates in version 10 of the Full Stack Developer training program.

Full announcement with a detailed FAQ about the certificate, the course, and the exams
Link: https://www.freecodecamp.org/news/freecodecamps-new-python-certification-is-now-live/

👉 @codeprogrammer
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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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🔥 NEW YEAR 2026 – PREMIUM SCIENTIFIC PAPER WRITING OFFER 🔥
Q1-Ready | Journal-Targeted | Publication-Focused

Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only.

To start 2026 strong, we’re offering a limited New Year scientific writing package designed for fast-track publication, not academic busywork.

🎯 What We Offer (End-of-Year Special):

✍️ Full Research Paper Writing – $400
(Q1 / Q2 journal–ready)

Includes:
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IMRAD structure (Introduction–Methods–Results–Discussion)
Strong problem formulation & novelty framing
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Plagiarism-free (Turnitin <10%)
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📊 Available Paper Types:

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Reviewer response (after review)

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Figure & table redesign (publication quality)

🚀 Why This Is Different
We don’t “write generic papers.”
We engineer publishable research.

✔️ Real novelty positioning
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Many of our papers are built on real-world datasets and are already aligned with Q1 journal standards.

New Year Offer – Limited Time

Regular price: $1,500 – $3,000

New Year 2026 price: $400

Limited slots (quality > quantity)

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📩 DM for details, samples & timelines
Contact:
@Omidyzd62

Start 2026 with a submitted paper—not just a plan
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Machine Learning with Python pinned «🔥 NEW YEAR 2026 – PREMIUM SCIENTIFIC PAPER WRITING OFFER 🔥 Q1-Ready | Journal-Targeted | Publication-Focused Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only. To start 2026 strong, we’re offering a limited New Year…»
🚀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 trannoscripts, 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://news.1rj.ru/str/CodeProgrammer 😅
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Forwarded from Code With Python
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
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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 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://news.1rj.ru/str/CodeProgrammer
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🚀 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://news.1rj.ru/str/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://news.1rj.ru/str/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.
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🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
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The first channel in Telegram that offers free Udemy coupons
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab

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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.
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⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY

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Admin: @HusseinSheikho
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