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.me/CodeProgrammer ✅

🚀 #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.me/CodeProgrammer ❤️

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.me/CodeProgrammer ✅

🌟 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.me/CodeProgrammer ⚡️

Good Morning 🌤

Real Python - Pocket Reference (Important)

#python #py #PythonTips #programming

https://t.me/CodeProgrammer 🩵

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.me/CodeProgrammer 🎀

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

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.me/CodeProgrammer

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.me/CodeProgrammer

It's both funny and sad... #memes

➡ @codeprogrammer

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.me/DataScienceM ✅