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Machine Learning with Python
前往频道在 Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Machine Learning with Python 的分析概览
频道 Machine Learning with Python (@codeprogrammer) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 67 828 名订阅者，在 教育 类别中位列第 2 402，并在 印度 地区排名第 5 082 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 67 828 名订阅者。
根据 03 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 63，过去 24 小时变化为 3，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 2.53%。内容发布后 24 小时内通常能获得 1.86% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 715 次浏览，首日通常累积 1 262 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 7。
- 主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 04 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
67 828
订阅者
+324 小时
+757 天
+6330 天
帖子存档
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.
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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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 💰
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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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
#ad 📢 InsideAd
