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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
Mostrar más📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 828 suscriptores, ocupando la posición 2 402 en la categoría Educación y el puesto 5 082 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 828 suscriptores.
Según los últimos datos del 03 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 63, y en las últimas 24 horas de 3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.53%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.86% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 715 visualizaciones. En el primer día suele acumular 1 262 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 04 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
67 828
Suscriptores
+324 horas
+757 días
+6330 días
Archivo de publicaciones
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.
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🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
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No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
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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
🧐 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 👩💻
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
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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 ✅
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.
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
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
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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@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
#ad 📢 InsideAd
