Convert any long article or PDF into a test in a couple of seconds!
Mini-service: we take the text of the article (or extract it from
First, we load the text of the material:
Next, we ask
๐ฅ Suitable for online courses, educational centers, and corporate training โ you immediately get a ready-made bank of tests from any article.
๐ช https://t.iss.one/CodeProgrammer
Mini-service: we take the text of the article (or extract it from
PDF), send it to GPT and receive a set of test questions with answer options and a key.First, we load the text of the material:
# article_text โ this is where we put the text of the article
with open("article.txt", "r", encoding="utf-8") as f:
article_text = f.read()
# for PDF, you can extract the text in advance with any library (PyPDF2, pdfplumber, etc.)
Next, we ask
GPT to generate a test:prompt = (
"You are an exam methodologist."
"Based on this text, create 15 test questions."
"Each question is in the format:\n"
"1) Question text\n"
"A. Option 1\n"
"B. Option 2\n"
"C. Option 3\n"
"D. Option 4\n"
"Correct answer: <letter>."
"Do not add explanations and comments, only questions, options, and correct answers."
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": article_text}
])
print(response.choices[0].message.content.strip())
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Machine Learning with Python
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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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.
๐ Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://t.iss.one/DataScienceMโ
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.
https://t.iss.one/DataScienceM
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I'm happy to announce that freeCodeCamp has launched a new certification in #Python ๐
ยป Learning the basics of programming
ยป Project development
ยป Final exam
ยป 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/
๐ @codeprogrammer
ยป Learning the basics of programming
ยป Project development
ยป Final exam
ยป 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/
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1. What will be the output of the following code?
A. [1] then [2]
B. [1] then [1, 2]
C. [] then []
D. Raises TypeError
Correct answer: A.
2. What is printed by this code?
A. 10
B. 5
C. None
D. UnboundLocalError
Correct answer: D.
3. What is the result of executing this code?
A. [1, 2, 3, 4]
B. [4]
C. [1, 2, 3]
D. []
Correct answer: C.
4. What does the following expression evaluate to?
A. False
B. True
C. Raises ValueError
D. None
Correct answer: B.
5. What will be the output?
A. <class 'list'>
B. <class 'set'>
C. <class 'dict'>
D. <class 'tuple'>
Correct answer: C.
6. What is printed by this code?
A. (1, 2, [3])
B. (1, 2, [3, 4])
C. TypeError
D. AttributeError
Correct answer: C.
7. What does this code output?
A. [0, 1, 2]
B. [1, 2]
C. [0]
D. []
Correct answer: B.
8. What will be printed?
A. None
B. KeyError
C. 2
D. "b"
Correct answer: C.
9. What is the output?
A. True True
B. True False
C. False True
D. False False
Correct answer: A.
10. What does this code produce?
A. 0 1
B. 1 2
C. 0 0
D. StopIteration
Correct answer: A.
11. What is printed?
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?
A. True True
B. False False
C. True False
D. False True
Correct answer: C.
13. What will be printed?
A. A
B. B
C. B then A
D. A then B
Correct answer: C.
14. What does this code output?
A. [1, 2, 3]
B. [3]
C. [1, 2]
D. Error
Correct answer: C.
15. What is printed?
A. <class 'list'>
B. <class 'tuple'>
C. <class 'generator'>
D. <class 'range'>
Correct answer: C.
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.
โค9๐1
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(Q1 / Q2 journalโready)
Includes:
โ Journal-targeted manuscript (Elsevier / Springer / Wiley / IEEE / MDPI)
โ IMRAD structure (IntroductionโMethodsโResultsโDiscussion)
โ Strong problem formulation & novelty framing
โ Methodology written to reviewer standards
โ Professional academic English (native-level)
โ Plagiarism-free (Turnitin <10%)
โ Ready for immediate submission
๐ Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
๐ง Optional Add-ons (if needed):
Journal selection & scope matching
Cover letter to editor
Reviewer response (after review)
Statistical validation & result polishing
Figure & table redesign (publication quality)
๐ Why This Is Different
We donโt โwrite generic papers.โ
We engineer publishable research.
โ๏ธ Real novelty positioning
โ๏ธ Reviewer-proof logic
โ๏ธ Data-driven arguments
โ๏ธ Aligned with current 2025โ2026 journal expectations
Many of our papers are built on real-world datasets and are already aligned with Q1 journal standards.
โณ New Year Offer โ Limited Time
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Universities & labs
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Machine Learning with Python pinned ยซ๐ฅ NEW YEAR 2026 โ PREMIUM SCIENTIFIC PAPER WRITING OFFER ๐ฅ Q1-Ready | Journal-Targeted | Publication-Focused Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only. To start 2026 strong, weโre offering a limited New Yearโฆยป
Forwarded from Machine Learning with Python
๐Stanford just completed a must-watch for anyone serious about AI:
๐ โ๐๐ ๐ ๐ฎ๐ต๐ฑ: ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ & ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐โ is now live entirely on YouTube and itโs pure gold.
If youโre building your AI career, stop scrolling.
This isnโt another surface-level overview. Itโs the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
๐ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ:
โข How Transformers actually work (tokenization, attention, embeddings)
โข Decoding strategies & MoEs
โข LLM finetuning (LoRA, RLHF, supervised)
โข Evaluation techniques (LLM-as-a-judge)
โข Optimization tricks (RoPE, quantization, approximations)
โข Reasoning & scaling
โข Agentic workflows (RAG, tool calling)
๐ง My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
๐ฅ 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
๐ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If youโre in AI โ whether building infra, agents, or apps โ this is the foundational course you donโt want to miss.
Letโs level up.
https://t.iss.one/CodeProgrammer๐
๐ โ๐๐ ๐ ๐ฎ๐ต๐ฑ: ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ & ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐โ is now live entirely on YouTube and itโs pure gold.
If youโre building your AI career, stop scrolling.
This isnโt another surface-level overview. Itโs the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
๐ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ:
โข How Transformers actually work (tokenization, attention, embeddings)
โข Decoding strategies & MoEs
โข LLM finetuning (LoRA, RLHF, supervised)
โข Evaluation techniques (LLM-as-a-judge)
โข Optimization tricks (RoPE, quantization, approximations)
โข Reasoning & scaling
โข Agentic workflows (RAG, tool calling)
๐ง My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
๐ฅ 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
๐ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If youโre in AI โ whether building infra, agents, or apps โ this is the foundational course you donโt want to miss.
Letโs level up.
https://t.iss.one/CodeProgrammer
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Forwarded from Code With Python
Automatic translator in Python!
We translate a text in a few lines using
Install the library:
Example of use:
Mass translation of a list:
๐ฅ 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.
๐ช @DataScience4
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("โ", GoogleTranslator(source="ru", target="es").translate(t))
๐ฅ 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.
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โค11๐ฅ1
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 -ยปยปยป a catalog of AI tools for all stages of research.
Inside:
-ยป working with literature
-ยป data analysis
-ยป turning articles into posters
-ยป automating experiments
-ยป tools for biology, chemistry, physics, and other fields
GitHub: https://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.
๐ https://t.iss.one/CodeProgrammer
On GitHub, there is a collection called Awesome AI for Science -ยปยปยป a catalog of AI tools for all stages of research.
Inside:
-ยป working with literature
-ยป data analysis
-ยป turning articles into posters
-ยป automating experiments
-ยป tools for biology, chemistry, physics, and other fields
GitHub: https://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.
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AI-ML Roadmap from Scratch
๐ https://github.com/aadi1011/AI-ML-Roadmap-from-scratch?tab=readme-ov-file
https://t.iss.one/CodeProgrammer๐
Like and Share
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๐ 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!
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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.iss.one/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA โ perfect for learning, coding, and mastering key programming skills.
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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
Your go-to hub for Kaggle datasets โ explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
Advancing research in Machine Learning โ practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
Explore the world of Data Science through Jupyter Notebooksโinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.iss.one/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.iss.one/DataScienceV
Dive into the world of Data Analytics โ uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
Master Python with step-by-step courses โ from basics to advanced projects and practical applications.
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