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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Numpy @CodeProgrammer.pdf
2.4 MB
🏷 Sections of the «NumPy» library
⬅️ From introductory to advanced


👨🏻‍💻 This is a long-term project to learn Python and NumPy from scratch. The main task is to handle numerical #data and #arrays in #Python using NumPy, and many other libraries are also used.


✏️ This section shows a structured and complete path for learning #NumPy; but the code examples and exercises help to practically memorize the concepts.


⭕️ Introduction to NumPy
🟠 NumPy arrays
⭕️ Introduction to array features
🟠 Basic operations on arrays
⭕️ Functions for statistical and aggregative purposes
🟠 And...

https://t.iss.one/CodeProgrammer ⚡️
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If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape.

https://t.iss.one/CodeProgrammer
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🚀 #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/CodeProgrammer ❤️
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Probability Distributions Cheat Sheet.pdf
2.6 MB
𝗜𝗳 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗶𝗻 𝘁𝗼𝘀𝘀𝗲𝘀…

Think again! 🎲

Here’s why it’s a game-changer for anyone in data science, analytics, and decision-making:

➜ Decode Uncertainty

From weather forecasts to financial markets, probability helps us make smarter choices.

➜ Master Essential Distributions

Understand Binomial, Poisson, Normal, and more in the simplest way possible.

➜ Crack Data Science Interviews

#Probability is a key topic in analytics and #machinelearning interviews.

➜ Avoid Common Misconceptions

Learn why "50-50 odds" don’t always mean a fair game.

➜ 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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https://t.iss.one/+YDWOxSLvMfQ2MGNi
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Mastering pandas%22.pdf
1.6 MB
🌟 A new and comprehensive book "Mastering pandas"

👨🏻‍💻 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.

⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.

🏷 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:

🔹 How to clean and prepare large amounts of data for analysis,

🔹 How to analyze real business data and draw conclusions,

🔹 How to automate repetitive tasks with a few lines of code,

🔹 And improve the speed and accuracy of your analyses significantly.

🌐 #DataScience #DataScience #Pandas #Python

https://t.iss.one/CodeProgrammer ⚡️
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Data Cleaning & Preprocessing Cheat Sheet

Essential Steps: Inputs, Outputs & Code

https://t.iss.one/CodeProgrammer 💙
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Good Morning 🌤
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Forwarded from Data Analytics
Want to get into Data Analysis?
Here are paid courses with certificates to build real skills:

1️⃣ Google Data Analytics Certificate
https://lnkd.in/dqEU-yht

2️⃣ IBM Data Science Certificate
https://lnkd.in/dQz58dY6

3️⃣ SQL Basics for Data Science
https://lnkd.in/dcFHHm28

4️⃣ Google Business Intelligence Certificate
https://lnkd.in/d4gbdF24

5️⃣ 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 🎀
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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
👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf

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

In the Solutions folder — the solutions
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions

👉 @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 😍

👉 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 — 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())


🔥 Suitable for online courses, educational centers, and corporate training — you immediately get a ready-made bank of tests from any article.

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

@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://t.iss.one/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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Forwarded from ADMINOTEKA
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