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Machine Learning with Python

Machine Learning with Python

@codeprogrammer

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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Post #4497 6.72K
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Post #4495 6.13K
Machine Learning with Python πŸ’Έ PacketSDK--A New Way To Make Revenue From Your Apps Regardless of whether your app is on desktop, mobile, TV, or Unity platforms, no matter which app monetization tools you’re using, PacketSDK can bring you additional revenue! ● Working Principle: Convert…
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Post #4491
Channel photo updated
Post #4488 7.28K
πŸ’Έ PacketSDK--A New Way To Make Revenue From Your Apps

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Post #4486 6.5K
Create a Wi-Fi QR code in Python in a couple of seconds

pip install wifi_qrcode_generator


import wifi_qrcode_generator.generator
from PIL import Image

ssid = "CLCoding_WIFI"
password = "supersecret123"
security = "WPA"

from wifi_qrcode_generator.generator import wifi_qrcode
qr = wifi_qrcode(ssid, False, security, password)

qr.make_image().save("wifi_qr.png")
Image.open("wifi_qr.png")


πŸ‘‰  @codeprogrammer
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Post #4483 6.97K
This GitHub repo is a gold mine for EVERY data scientist!

(full of hands-on and interactive tutorials)

DS Interactive Python provides several dashboards to interactively learn about statistics, ML models, and other related theoretical concepts!

Some key topics you can understand interactively:
β€’ PCA
β€’ Bagging and boosting
β€’ Linear regression and OLS
β€’ Bayesian and frequentist statistics
β€’ confidence intervals
β€’ clustering (kmeans, spectral clustering, etc.)
β€’ central limit theorem
β€’ neural networks (the backpropagation animation is really good)
β€’ and many more.

Link: https://github.com/GeostatsGuy/DataScienceInteractivePython

https://t.me/CodeProgrammer
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Post #4475 6.82K
Data Science Formulas Cheat Sheet.pdf175.4 KB
🏷 Data Science Formulas Cheat Sheet
βž• Application of Each Formula

πŸ‘¨πŸ»β€πŸ’» This cheat sheet presents important data science concepts along with their formulas.

βœ… From key topics in statistics to machine learning and NLP.

βœ… And the main formulas that are always needed + real examples for each formula, showing you when and why to use each method.

🌐 #Data_Science #DataScience

https://t.me/CodeProgrammer πŸ”°

More Likes Please πŸ–•
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Post #4469 5.9K
Comprehensive Python Cheatsheet.pdf6.3 MB
Comprehensive Python Cheatsheet

This Comprehensive #Python Cheatsheet brings together core syntax, data structures, functions, #OOP, decorators, regular expressions, libraries, and more β€” neatly organized for quick reference and deep understanding.

https://t.me/CodeProgrammer
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Post #4465 5.46K
The difference between import os and from os import *
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Post #4464 5.42K

Forwarded from Machine Learning

πŸ“Œ PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2025-11-19 | ⏱️ Read time: 14 min read

Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.

#PyTorch #MachineLearning #NeuralNetwork #Regression #Python
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Post #4458 6.51K
Stochastic and deterministic sampling methods in diffusion models produce noticeably different trajectories, but ultimately both reach the same goal.

Diffusion Explorer allows you to visually compare different sampling methods and training objectives of diffusion models by creating visualizations like the one in the 2 videos.

Additionally, you can, for example, train a model on your own dataset and observe how it gradually converges to a sample from the correct distribution.

Check out this GitHub repository:
https://github.com/helblazer811/Diffusion-Explorer

πŸ‘‰ https://t.me/CodeProgrammer
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Post #4455 7.68K
Tip for clean code in Python:

Use Dataclasses for classes that primarily store data. The @dataclass decorator automatically generates special methods like __init__(), __repr__(), and __eq__(), reducing boilerplate code and making your intent clearer.

from dataclasses import dataclass

# --- BEFORE: Using a standard class ---
# A lot of boilerplate code is needed for basic functionality.

class ProductOld:
def __init__(self, name: str, price: float, sku: str):
self.name = name
self.price = price
self.sku = sku

def __repr__(self):
return f"ProductOld(name='{self.name}', price={self.price}, sku='{self.sku}')"

def __eq__(self, other):
if not isinstance(other, ProductOld):
return NotImplemented
return (self.name, self.price, self.sku) == (other.name, other.price, other.sku)

# Example Usage
product_a = ProductOld("Laptop", 1200.00, "LP-123")
product_b = ProductOld("Laptop", 1200.00, "LP-123")

print(product_a) # Output: ProductOld(name='Laptop', price=1200.0, sku='LP-123')
print(product_a == product_b) # Output: True


# --- AFTER: Using a dataclass ---
# The code is concise, readable, and less error-prone.

@dataclass(frozen=True) # frozen=True makes instances immutable
class Product:
name: str
price: float
sku: str

# Example Usage
product_c = Product("Laptop", 1200.00, "LP-123")
product_d = Product("Laptop", 1200.00, "LP-123")

print(product_c) # Output: Product(name='Laptop', price=1200.0, sku='LP-123')
print(product_c == product_d) # Output: True


#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality

━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨
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Post #4445 7.45K
Brought an awesome repo for those who love learning from real examples. It contains over a hundred open-source clones of popular services: from Airbnb to YouTube

Each project is provided with links to the source code, demos, stack description, and the number of stars on GitHub. Some even have tutorials on how to create them

Grab it on GitHub 🍯: https://github.com/gorvgoyl/clone-wars

πŸ‘‰ https://t.me/CodeProgrammer
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