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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Network:📈 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 68 117 subscribers, ranking 2 375 in the Education category and 4 809 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 117 subscribers.
According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 120 over the last 30 days and by -14 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.55%. Within the first 24 hours after publication, content typically collects 2.02% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 099 views. Within the first day, a publication typically gains 1 378 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- 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 27 August, 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.
68 117
Subscribers
-1424 hours
-667 days
+12030 days
Posts Archive
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 ✅
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.
🧐 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
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
🧮 $40/day × 30 days = $1,200/month.
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Hugging Face has literally gathered all the key "secrets". 🤔
It's important to understand the evaluation of large language models. 📊
While you're working with language models:
> training or retraining your models, 🔄
> selecting a model for a task, 🎯
> or trying to understand the current state of the field, 🌍
the question almost inevitably arises:
how to understand that a model is good? ❓
The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings, 🏆
> benchmarks that supposedly measure reasoning, 🧠
> knowledge, coding or mathematics, 💻
> articles with claimed new best results. 📈
But what is evaluation actually? 🤷
And what does it really show? 🔍
This guide helps to understand everything. 📚
What is model evaluation all about 🤖
Basic concepts of large language models for understanding evaluation 🏗️
Evaluation through ready-made benchmarks 📏
Creating your own evaluation system 🔧
The main problem of evaluation ⚠️
Evaluation of free text 📝
Statistical correctness of evaluation 📉
Cost and efficiency of evaluation 💰
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
this channel for sale 500$
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contact me
@HusseinSheikho
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?
Most AI engineers never fully understood the maths behind what they build! 🤯🧮
This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨
Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗
What it covers:
- Vectors, linear algebra, calculus, and optimization 📐📉
- Classical machine learning and deep learning 🤖
- Transformer architectures and LLMs 🦄
- Efficient architectures, quantization, and distillation ⚡️
- CUDA, GPU programming, and SIMD 🚀
- AI inference and deployment 🌐
Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗
Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗
🧮 $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
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🔖 A huge repository of resources on Data Science 📈
Awesome DataScience — a structured list of open-source data, datasets, libraries, and tutorials for solving real-world problems. 🛠️
It's useful for both beginners and those already familiar with the field — you'll find something new here. 🌱
⛓️ Link to GitHub: https://github.com/academic/awesome-datascience 🔗
tags: #DataScientist 🤖 #AI 🧠 #TechCommunity 🌐 #GrowthMindset 📈 #OpenSource 🏆
▶️ https://t.me/CodeProgrammer 👨💻
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