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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Mastering pandas%22.pdf
1.6 MB
๐ŸŒŸ A new and comprehensive book "Mastering pandas"

๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.

โฌ…๏ธ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.

๐Ÿท This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:

๐Ÿ”น How to clean and prepare large amounts of data for analysis,

๐Ÿ”น How to analyze real business data and draw conclusions,

๐Ÿ”น How to automate repetitive tasks with a few lines of code,

๐Ÿ”น And improve the speed and accuracy of your analyses significantly.

๐ŸŒ #DataScience #DataScience #Pandas #Python

https://t.iss.one/CodeProgrammer โšก๏ธ
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Data Cleaning & Preprocessing Cheat Sheet

Essential Steps: Inputs, Outputs & Code

https://t.iss.one/CodeProgrammer ๐Ÿ’™
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Good Morning ๐ŸŒค
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Forwarded from Data Analytics
Want to get into Data Analysis?
Here are paid courses with certificates to build real skills:

1๏ธโƒฃ Google Data Analytics Certificate
https://lnkd.in/dqEU-yht

2๏ธโƒฃ IBM Data Science Certificate
https://lnkd.in/dQz58dY6

3๏ธโƒฃ SQL Basics for Data Science
https://lnkd.in/dcFHHm28

4๏ธโƒฃ Google Business Intelligence Certificate
https://lnkd.in/d4gbdF24

5๏ธโƒฃ Microsoft Python Development Certificate
https://lnkd.in/dDXX_AHM

Which data skill are you focusing on now?
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Machine Learning Fundamentals.pdf
22.6 MB
Machine Learning Fundamentals

A structured Machine Learning Fundamentals guide covering core concepts, intuition, math basics, ML algorithms, deep learning, and real-world workflows.


https://t.iss.one/CodeProgrammer ๐ŸŽ€
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I rarely say this, but this is the best repository for mastering Python.

The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).

In this PythonMastery.pdf, all the information is structured
๐Ÿ‘พ Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf

In the Exercises folder, all the exercises are located
๐Ÿ‘พ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises

In the Solutions folder โ€” the solutions
๐Ÿ‘พ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions

๐Ÿ‘‰ @codeprogrammer
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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 ๐Ÿ˜

๐Ÿ‘‰ https://t.iss.one/CodeProgrammer
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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 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())


๐Ÿ”ฅ Suitable for online courses, educational centers, and corporate training โ€” you immediately get a ready-made bank of tests from any article.

๐Ÿšช https://t.iss.one/CodeProgrammer
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It's both funny and sad... #memes

โžก @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.

๐Ÿ–• Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub

https://t.iss.one/DataScienceM โœ…
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I'm happy to announce that freeCodeCamp has launched a new certification in #Python ๐Ÿ

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/

๐Ÿ‘‰ @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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Start 2026 with a submitted paperโ€”not just a plan
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Machine Learning with Python pinned ยซ๐Ÿ”ฅ NEW YEAR 2026 โ€“ PREMIUM SCIENTIFIC PAPER WRITING OFFER ๐Ÿ”ฅ Q1-Ready | Journal-Targeted | Publication-Focused Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only. To start 2026 strong, weโ€™re offering a limited New Yearโ€ฆยป
๐Ÿš€Stanford just completed a must-watch for anyone serious about AI:

๐ŸŽ“ โ€œ๐—–๐— ๐—˜ ๐Ÿฎ๐Ÿต๐Ÿฑ: ๐—ง๐—ฟ๐—ฎ๐—ป๐˜€๐—ณ๐—ผ๐—ฟ๐—บ๐—ฒ๐—ฟ๐˜€ & ๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€โ€ is now live entirely on YouTube and itโ€™s pure gold.

If youโ€™re building your AI career, stop scrolling.
This isnโ€™t another surface-level overview. Itโ€™s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.

๐Ÿ“š ๐—ง๐—ผ๐—ฝ๐—ถ๐—ฐ๐˜€ ๐—ฐ๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ฒ๐—ฑ ๐—ถ๐—ป๐—ฐ๐—น๐˜‚๐—ฑ๐—ฒ:
โ€ข How Transformers actually work (tokenization, attention, embeddings)
โ€ข Decoding strategies & MoEs
โ€ข LLM finetuning (LoRA, RLHF, supervised)
โ€ข Evaluation techniques (LLM-as-a-judge)
โ€ข Optimization tricks (RoPE, quantization, approximations)
โ€ข Reasoning & scaling
โ€ข Agentic workflows (RAG, tool calling)

๐Ÿง  My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโ€™ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.

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- 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

๐Ÿ—“ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.

If youโ€™re in AI โ€” whether building infra, agents, or apps โ€” this is the foundational course you donโ€™t want to miss.

Letโ€™s level up.
https://t.iss.one/CodeProgrammer ๐Ÿ˜…
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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("โ†’", GoogleTranslator(source="ru", target="es").translate(t))


๐Ÿ”ฅ 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.

๐Ÿšช @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 -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.

๐Ÿ‘‰ https://t.iss.one/CodeProgrammer
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