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The #Python library #PandasAI has been released for simplified data analysis using AI.
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link😍
👉 https://news.1rj.ru/str/CodeProgrammer
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link
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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
🔥 Suitable for online courses, educational centers, and corporate training — you immediately get a ready-made bank of tests from any article.
🚪 https://news.1rj.ru/str/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 — 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
Admin: @HusseinSheikho || @Hussein_Sheikho
❤7👍2
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✅
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://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
» 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/
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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.
❤7👍1
Forwarded from ADMINOTEKA
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!
Привет, давай знакомиться. Не самое скромное приветствие получилось, но мы можем подтвердить свои слова.
💸 Подключай свой канал и проверяй на практике. Преврати свой канал в реальный доход вместе с нами!
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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:
✅ Journal-targeted manunoscript (Elsevier / Springer / Wiley / IEEE / MDPI)
✅ IMRAD structure (Introduction–Methods–Results–Discussion)
✅ Strong problem formulation & novelty framing
✅ Methodology written to reviewer standards
✅ Professional academic English (native-level)
✅ Plagiarism-free (Turnitin <10%)
✅ Ready for immediate submission
📊 Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
🧠 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)
🚀 Why This Is Different
We don’t “write generic papers.”
We engineer publishable research.
✔️ Real novelty positioning
✔️ Reviewer-proof logic
✔️ Data-driven arguments
✔️ Aligned with current 2025–2026 journal expectations
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)
🎓 Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
📩 DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paper—not just a plan
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:
✅ Journal-targeted manunoscript (Elsevier / Springer / Wiley / IEEE / MDPI)
✅ IMRAD structure (Introduction–Methods–Results–Discussion)
✅ Strong problem formulation & novelty framing
✅ Methodology written to reviewer standards
✅ Professional academic English (native-level)
✅ Plagiarism-free (Turnitin <10%)
✅ Ready for immediate submission
📊 Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
🧠 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)
🚀 Why This Is Different
We don’t “write generic papers.”
We engineer publishable research.
✔️ Real novelty positioning
✔️ Reviewer-proof logic
✔️ Data-driven arguments
✔️ Aligned with current 2025–2026 journal expectations
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)
🎓 Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
📩 DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paper—not just a plan
❤4🔥3
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…»
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
Ничего не продает, просто куча трушных постов про маркетинг и конечно же мемы (а куда без них). Читайте: @maratyus
💯4
Forwarded from Machine Learning with Python
🚀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😅
🎓 “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
Install the library:
Example of use:
Mass translation of a list:
🔥 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
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.
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❤3