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 078 Education2 407...

📈 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 67 827 subscribers, ranking 2 407 in the Education category and 5 078 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 827 subscribers.

According to the latest data from 04 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 75 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.53%. Within the first 24 hours after publication, content typically collects 1.84% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 717 views. Within the first day, a publication typically gains 1 249 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 05 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.

67 827

Subscribers

+1124 hours

+587 days

+7530 days

1 717

Post views

~ 1 24924 hours

~ 1 70648 hours

2.53%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

67 827

Repost from Learn Python Coding

Master Python the Right Way – Without Procrastination. 🐍✨ When I first started learning Python, I quickly realized: You can't master a programming language just by reading syntax or watching tutorials. 📚🚫 Real growth happens when you practice, build, and solve problems on your own. 🛠💻 That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠 What is this collection about? 🤔 ✔️ Beginner to advanced programs with clear explanations ✔️ Pattern-based exercises to strengthen core fundamentals ✔️ Problem-solving programs that sharpen logical thinking Why is this important? 🌟 You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️ This is perfect for: 🎯 • Preparing for technical interviews 🤝 • Participating in coding challenges 🏆 • Building real-world Python projects 🚀 https://t.me/pythonRe

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🧮 $40/day × 30 days = $1,200/month. That's what my students average. From their phone. In 10 minutes a day. No degree needed. No investment knowledge required. Just Copy & Paste my moves. I'm Tania, and this is real. 👉 Join for Free, Click here #ad 📢 InsideAd

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Most traders lose because they don’t manage risk properly. I run a system focused on steady growth and capital protection. No gambling, no unrealistic promises. #ad 📢 InsideAd

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This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳ It includes: ✅ Supervised & Unsupervised algorithms ✅ Regression, Classification & Clustering techniques ✅ PCA & Dimensionality Reduction ✅ Neural Networks, CNN, RNN & Transformers ✅ Assumptions, Pros/Cons & Real-world use cases Whether you're: 🔹 Preparing for data science interviews 🔹 Working on ML projects 🔹 Or strengthening your fundamentals this one-page guide is a must-save. ♻️ Repost and share with your ML circle. #MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML

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🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work! The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code. Repo: https://github.com/onyxwizard/python-cheatsheet https://t.me/pythonRe 👩‍💻

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In the last 9 market sessions, 6 “perfect” Telegram signals would’ve lost… by 3 pips or less. Listen - that’s not bad luck. That’s sloppy entries + no trade plan. Inside 𝗘𝗟𝗜𝗧𝗘𝗣𝗜𝗣 𝗘𝗠𝗣𝗜𝗥𝗘 ️️📊 you get: - 📊 daily setups + levels that actually matter - 🧠 market context (so you stop guessing) - 🤝 1-on-1 mentorship when you’re stuck Request access: Join Apply #ad 📢 InsideAd

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Stop asking "CNN or VLM?" — the answer is both. 🤔 Everyone's talking about Vision Language Models replacing traditional computer vision. 📢 Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀 CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯 Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠 The smartest production systems combine both: → A lightweight CNN runs first (fast, cheap) ⚡️ → A VLM handles the complex reasoning (flexible, expensive) 💎 This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣 Dr. Satya Mallick breaks it down in under 2 minutes. 👇 #ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering https://t.me/CodeProgrammer ✅

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https://t.me/PaperNexus Your path to exploring the latest topics in artificial intelligence and machine learning, and where the world stands in terms of updates. Don't be backward and distant from the people.

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🧐 Confusion Matrix: Less confusing 🤯 Many data science beginners struggle to understand true negative (TN), false negative (FN), false positive (FP), and true positive (TP). 🤔 You can easily understand the values using the confusion matrix. 📊 💡 It is a 2x2 matrix for a binary classifier: - True Negative (TN): True Negative prediction ✅ - False Negative (FN): False Negative prediction ❌ - False Positive (FP): False Positive prediction 🚨 - True Positive (TP): True Positive prediction 🎯 ❓ For each prediction, ask two questions: 1. Did the model do it right? Yes (True) or No (False) 2. What was the predicted class? Positive or Negative

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Repost from Machine Learning

Algorithms by Jeff Erickson - one of the best algorithm books out there 📚. The illustrations make complex concepts surprisingly easy to follow 🎨. Highly recommend this 👍. Link: https://jeffe.cs.illinois.edu/teaching/algorithms/ 🔗 https://t.me/MachineLearning9

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Most traders lose because they don’t manage risk properly. I run a system focused on steady growth and capital protection. No gambling, no unrealistic promises. Want me to share a recent result and how it was achieved? #ad 📢 InsideAd

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https://t.me/pythonRe

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https://t.me/MachineLearning9

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Hugging Face has literally gathered all the key "secrets". 🤔 It's important to understand the evaluation of large language models. 📊 While you're working with language models: > training or retraining your models, 🔄 > selecting a model for a task, 🎯 > or trying to understand the current state of the field, 🌍 the question almost inevitably arises: how to understand that a model is good? ❓ The answer is quality evaluation. It's everywhere: > leaderboards with model ratings, 🏆 > benchmarks that supposedly measure reasoning, 🧠 > knowledge, coding or mathematics, 💻 > articles with claimed new best results. 📈 But what is evaluation actually? 🤷 And what does it really show? 🔍 This guide helps to understand everything. 📚 What is model evaluation all about 🤖 Basic concepts of large language models for understanding evaluation 🏗️ Evaluation through ready-made benchmarks 📏 Creating your own evaluation system 🔧 The main problem of evaluation ⚠️ Evaluation of free text 📝 Statistical correctness of evaluation 📉 Cost and efficiency of evaluation 💰

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Your 1:3 RR keeps failing for 7 days? 📊 ElitePIP “Entry Filters”: 3 checks before you click. Get it: Join Filters #ad 📢 InsideAd

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this channel for sale 500$ https://t.me/DataScienceN contact me @HusseinSheikho

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Overfitting and Generalization in Machine Learning My ML model had 100% accuracy. And was completely useless. That's not a paradox; that's overfitting. The model didn't learn. It memorized. Here's the mathematical core most tutorials skip: E[loss] = Bias² + Variance + σ² → Bias² = too simple → Underfitting → Variance = too complex → Overfitting → σ² = irreducible → always there What this actually means in practice: → A degree-9 polynomial on 6 data points hits R² = 1.0 and oscillates wildly between them → A linear model on sine-wave data has near-zero variance — but massive bias → The optimal model isn't the simplest. Not the most complex. It's the one minimizing Bias² + Variance And the generalization gap? Formally defined as: gen_gap(f) = R(f) − R_emp(f) When this value is ≫ 0, your model is learning noise, not signal. The fix isn't "collect more data and hope." The fix is regularization, which I derive fully in my paper: L1, L2, Dropout, and Early Stopping, all from first principles. Which regularization strategy do you use most and why?