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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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 117 obunachidan iborat bo'lib, Taʼlim toifasida 2 375-o'rinni va Hindiston mintaqasida 4 809-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
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- Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.
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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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
68 117
Obunachilar
-1424 soatlar
-667 kunlar
+12030 kunlar
Postlar arxiv
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
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Your path to exploring the latest topics in artificial intelligence and machine learning, and where the world stands in terms of updates.
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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
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/ 🔗
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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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@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. 🌱
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