๐ #Pandas Cheat Sheet for Everyday Data Work
This covers the essential functions we use in day to day work like inspecting data, selecting rows and columns, cleaning, manipulating and doing quick aggregations.
https://t.iss.one/CodeProgrammerโค๏ธ
This covers the essential functions we use in day to day work like inspecting data, selecting rows and columns, cleaning, manipulating and doing quick aggregations.
https://t.iss.one/CodeProgrammer
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Probability Distributions Cheat Sheet.pdf
2.6 MB
๐๐ณ ๐๐ผ๐ ๐๐ต๐ถ๐ป๐ธ ๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐ถ๐ ๐ท๐๐๐ ๐ฎ๐ฏ๐ผ๐๐ ๐ฐ๐ผ๐ถ๐ป ๐๐ผ๐๐๐ฒ๐โฆ
Think again! ๐ฒ
Hereโs why itโs a game-changer for anyone in data science, analytics, and decision-making:
โ Decode Uncertainty
From weather forecasts to financial markets, probability helps us make smarter choices.
โ Master Essential Distributions
Understand Binomial, Poisson, Normal, and more in the simplest way possible.
โ Crack Data Science Interviews
#Probability is a key topic in analytics and #machinelearning interviews.
โ Avoid Common Misconceptions
Learn why "50-50 odds" donโt always mean a fair game.
โ Visualize Concepts, Not Just Formulas
The best way to learn is through intuitive graphs and real-world examples!
https://t.iss.one/CodeProgrammerโ
Think again! ๐ฒ
Hereโs why itโs a game-changer for anyone in data science, analytics, and decision-making:
โ Decode Uncertainty
From weather forecasts to financial markets, probability helps us make smarter choices.
โ Master Essential Distributions
Understand Binomial, Poisson, Normal, and more in the simplest way possible.
โ Crack Data Science Interviews
#Probability is a key topic in analytics and #machinelearning interviews.
โ Avoid Common Misconceptions
Learn why "50-50 odds" donโt always mean a fair game.
โ Visualize Concepts, Not Just Formulas
The best way to learn is through intuitive graphs and real-world examples!
https://t.iss.one/CodeProgrammer
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โ๏ธLISA HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY!
Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel
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โก๏ธFREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! ๐๐
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Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel
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โค5๐ฅ5
Mastering pandas%22.pdf
1.6 MB
๐จ๐ปโ๐ป 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.
https://t.iss.one/CodeProgrammer
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Data Cleaning & Preprocessing Cheat Sheet
Essential Steps: Inputs, Outputs & Code
https://t.iss.one/CodeProgrammer๐
Essential Steps: Inputs, Outputs & Code
https://t.iss.one/CodeProgrammer
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Real Python.pdf
332 KB
Real Python - Pocket Reference (Important)
#python #py #PythonTips #programming
https://t.iss.one/CodeProgrammer๐ฉต
#python #py #PythonTips #programming
https://t.iss.one/CodeProgrammer
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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๏ธโฃ Google Data Analytics Certificate
https://lnkd.in/dqEU-yht
2๏ธโฃ IBM Data Science Certificate
https://lnkd.in/dQz58dY6
3๏ธโฃ SQL Basics for Data Science
https://lnkd.in/dcFHHm28
4๏ธโฃ Google Business Intelligence Certificate
https://lnkd.in/d4gbdF24
5๏ธโฃ Microsoft Python Development Certificate
https://lnkd.in/dDXX_AHM
Which data skill are you focusing on now?
Here are paid courses with certificates to build real skills:
1๏ธโฃ Google Data Analytics Certificate
https://lnkd.in/dqEU-yht
2๏ธโฃ IBM Data Science Certificate
https://lnkd.in/dQz58dY6
3๏ธโฃ SQL Basics for Data Science
https://lnkd.in/dcFHHm28
4๏ธโฃ Google Business Intelligence Certificate
https://lnkd.in/d4gbdF24
5๏ธโฃ Microsoft Python Development Certificate
https://lnkd.in/dDXX_AHM
Which data skill are you focusing on now?
โค8
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/CodeProgrammer๐
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
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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
๐พ Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf
In the Exercises folder, all the exercises are located
๐พ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises
In the Solutions folder โ the solutions
๐พ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions
๐ @codeprogrammer
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
In the Exercises folder, all the exercises are located
In the Solutions folder โ the solutions
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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 link๐
๐ https://t.iss.one/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
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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Telegram
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
โค7๐2
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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Start 2026 with a submitted paperโnot just a plan
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 Year scientific writing package designed for fast-track publication, not academic busywork.
๐ฏ What We Offer (End-of-Year Special):
โ๏ธ Full Research Paper Writing โ $400
(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
Regular price: $1,500 โ $3,000
New Year 2026 price: $400
Limited slots (quality > quantity)
๐ Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
๐ฉ DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paperโnot just a plan
โค6๐ฅ3
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