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

Network:Machine Learning with Python India5 049 Education2 404...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 821 subscribers, ranking 2 404 in the Education category and 5 049 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 821 subscribers.

According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.60%. Within the first 24 hours after publication, content typically collects 2.50% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 767 views. Within the first day, a publication typically gains 1 695 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 07 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 821

Subscribers

+924 hours

+587 days

+7730 days

1 767

Post views

~ 1 69524 hours

~ 2 25748 hours

2.60%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

67 824

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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.

https://t.me/CodeProgrammer

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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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🤖🧠 Context Engineering 2.0: Redefining Human–Machine Understanding 🗓️ 16 Nov 2025 📚 AI News & Trends As artificial intelligence advances, machines are becoming increasingly capable of understanding and responding to human language. Yet, one crucial challenge remains how can machines truly understand the context behind human intentions? This question forms the foundation of context engineering, a discipline that focuses on designing, organizing and managing contextual information so that AI systems can ... #ContextEngineering #AIEducation #HumanMachineUnderstanding #AIContext #NaturalLanguageProcessing #AIModels

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🤖🧠 OpenAI Evals: The Framework Transforming LLM Evaluation and Benchmarking 🗓️ 16 Nov 2025 📚 AI News & Trends As large language models (LLMs) continue to reshape industries from education and healthcare to marketing and software development – the need for reliable evaluation methods has never been greater. With new models constantly emerging, developers and researchers require a standardized system to test, compare and understand model performance across real-world scenarios. This is where OpenAI ... #OpenAIEvals #LLMEvaluation #Benchmarking #LargeLanguageModels #AIResearch #ModelEvaluation

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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