Cheatsheet Machine Learning Algorithms🌟

🔰 Convert decimals to other number system

❌ YOU CAN'T USE LAMBDA LIKE THIS IN PYTHON

The main mistake is turning lambda into a logic dump: adding side effects, print calls, long conditions, and calculations to it.

Such lambdas are hard to read, impossible to debug properly, and they violate the very idea of being a short and clean function. Everything complex should be moved into a regular function. Subscribe for more tips every day !

# you can't do this - lambda with state changes
data = [1, 2, 3]
logs = []

# dangerous antipattern
process = lambda x: logs.append(f"processed {x}") or (x * 10)

result = [process(n) for n in data]

print("RESULT:", result)
print("LOGS:", logs)

- Location of Mobile Number Code -

import phonenumbers
from phonenumbers import timezone
from phonenumbers import geocoder
from phonenumbers import carrier

number = input("Enter the phone number with country code : ")

# Parsing String to the Phone number
phoneNumber = phonenumbers.parse(number)

# printing the timezone using the timezone module
timeZone = timezone.time_zones_for_number(phoneNumber)
print("timezone : "+str(timeZone))

# printing the geolocation of the given number using the geocoder module
geolocation = geocoder.description_for_number(phoneNumber,"en")
print("location : "+geolocation)

# printing the service provider name using the carrier module
service = carrier.name_for_number(phoneNumber,"en")
print("service provider : "+service)

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.

✅ 📚 Python Libraries You Should Know

1. NumPy – Numerical computing
- Arrays, matrices, broadcasting
- Fast operations on large datasets
- Useful in data science & ML

2. Pandas – Data analysis & manipulation
- DataFrames and Series
- Reading/writing CSV, Excel
- GroupBy, filtering, merging

3. Matplotlib – Data visualization
- Line, bar, pie, scatter plots
- Custom styling & labels
- Save plots as images

4. Seaborn – Statistical plotting
- Built on Matplotlib
- Heatmaps, histograms, violin plots
- Great for EDA

5. Requests – HTTP library
- Make GET, POST requests
- Send headers, params, and JSON
- Used in web scraping and APIs

6. BeautifulSoup – Web scraping
- Parse HTML/XML easily
- Find elements using tags, class
- Navigate and extract data

7. Flask – Web development microframework
- Lightweight and fast
- Routes, templates, API building
- Great for small to medium apps

8. Django – High-level web framework
- Full-stack: ORM, templates, auth
- Scalable and secure
- Ideal for production-ready apps

9. SQLAlchemy – ORM for databases
- Abstract SQL queries in Python
- Connect to SQLite, PostgreSQL, etc.
- Schema creation & query chaining

10. Pytest – Testing framework
- Simple syntax for test cases
- Fixtures, asserts, mocking
- Supports plugins

11. Scikit-learn – Machine Learning
- Preprocessing, classification, regression
- Train/test split, pipelines
- Built on NumPy & Pandas

12. TensorFlow / PyTorch – Deep learning
- Neural networks, backpropagation
- GPU support
- Used in real AI projects

13. OpenCV – Computer vision
- Image processing, face detection
- Filters, contours, image transformations
- Real-time video analysis

14. Tkinter – GUI development
- Build desktop apps
- Buttons, labels, input fields
- Easy drag-and-drop interface

Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1885

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Python Roadmap 🐍 (Beginner → Job)

📂 Syntax Basics
∟ Variables, Data Types
∟ Conditions & Loops

📂 Data Structures
∟ List, Tuple, Set, Dict
∟ Comprehensions

📂 Algorithms
∟ Searching & Sorting
∟ Recursion & Big-O

📂 OOP Concepts
∟ Class & Object
∟ Inheritance & Polymorphism

📂 Modules & Errors
∟ Import & pip
∟ try / except

📂 File Handling
∟ Read / Write Files
∟ CSV & JSON

📂 Networking
∟ APIs & Requests
∟ JSON Data

📂 Security Basics
∟ Password Hashing
∟ API Keys

📂 Practice & Projects
∟ Mini Programs
∟ Real Projects

∟✅ Job / Internship Ready

❤️ React for more Python roadmaps
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Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape

🔘Pro is currently the #1 open-source model worldwide
🔘Lite (2B parameters) outperforms Sora v1.
🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21.

Useful links
🔘Full leaderboard: LM Arena
🔘Kandinsky 5.0 details: technical report
🔘Open-source Kandinsky 5.0: GitHub and Hugging Face