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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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 820 obunachidan iborat bo'lib, Taʼlim toifasida 2 411-o'rinni va Hindiston mintaqasida 5 035-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 67 820 obunachiga ega bo‘ldi.

06 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 55 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 2.54% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.53% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 1 720 marta ko‘riladi; birinchi sutkada odatda 1 714 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 6 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.

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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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 08 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

67 820

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+5530 kunlar

1 720

Post ko'rishlar

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2.54%

Muloqot nisbati

~ 3

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

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Comprehensive Python Cheatsheet.pdf6.30 MB

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Repost from Machine Learning

📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch 🗂 Category: DEEP LEARNING 🕒 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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Repost from Free Online Courses

📚 Free Course: Advanced SQL from Kaggle 🏢 Provider: Kaggle 💰 Pricing: Free Certificate 🌍 Language: English ⏱ Duration: 4 hours 📅 Sessions: On-Demand 📊 Level: Intermediate ✨ By: @DataScienceV

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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 👉 https://t.me/CodeProgrammer

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direct message https://t.me/DataScienceY?direct

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Repost from Free Online Courses

https://t.me/DataScienceY

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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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https://t.me/DataScienceC

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https://t.me/DataScienceY

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🤖🧠 Skyvern: The Future of Browser Automation Powered by AI and Computer Vision 🗓️ 16 Nov 2025 📚 AI News & Trends In today’s fast-evolving digital landscape, automation plays a crucial role in enhancing productivity, efficiency and innovation. Yet, traditional browser automation tools often struggle with complexity, maintenance and reliability. They rely heavily on DOM parsing, XPaths and rigid scripts that easily break when websites change their layout. Enter Skyvern, an open-source, AI-driven browser automation platform developed ... #Skyvern #BrowserAutomation #AIDriven #ComputerVision #OpenSource #WebAutomation