Machine Learning

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Updated: May 26, 2026 at 6:28 PM

Machine Learning

Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications. Admin: @HusseinSheikho || @Hussein_Sheikho

@DataScienceM offers specialized information about insights and clear for subscribers interested in Courses & Learning

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Machine Learning

May 16, 2026, 12:04 PM

Channel created

Machine Learning

May 16, 2026, 12:04 PM

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116

Machine Learning

Apr 2, 2026, 07:29 AM

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📌 Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-03-30 | ⏱️ Read time: 16 min read

SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after…

#DataScience #AI #Python

874

Machine Learning

Apr 2, 2026, 07:29 AM

📷 Photo

📌 How to Lie with Statistics with your Robot Best Friend

🗂 Category: SCIENCE

🕒 Date: 2026-03-30 | ⏱️ Read time: 12 min read

What is p hacking, is it bad, and can you get ai to do it…

#DataScience #AI #Python

777

Machine Learning

Apr 2, 2026, 07:29 AM

📷 Photo

📌 Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining

🗂 Category: DEEP LEARNING

🕒 Date: 2026-03-29 | ⏱️ Read time: 22 min read

What happens when your production model drifts and retraining isn’t an option? This article shows…

#DataScience #AI #Python

968

Machine Learning

Apr 2, 2026, 07:29 AM

📷 Photo

📌 How to Become an AI Engineer Fast (Skills, Projects, Salary)

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-29 | ⏱️ Read time: 12 min read

Spoiler, it will take longer than 3 months

#DataScience #AI #Python

935

Machine Learning

Apr 2, 2026, 07:29 AM

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📌 From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis

🗂 Category: CLIMATE CHANGE

🕒 Date: 2026-03-28 | ⏱️ Read time: 7 min read

Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow

#DataScience #AI #Python

1,050

Machine Learning

Apr 2, 2026, 07:29 AM

📷 Photo

📌 Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-28 | ⏱️ Read time: 25 min read

It’s easier than ever to 10x your output with agentic AI.

#DataScience #AI #Python

1,070

Machine Learning

Apr 2, 2026, 07:29 AM

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📌 A Beginner’s Guide to Quantum Computing with Python

🗂 Category: QUANTUM COMPUTING

🕒 Date: 2026-03-27 | ⏱️ Read time: 7 min read

Simulate a quantum computer with Qiskit

#DataScience #AI #Python

1,030

Machine Learning

Apr 2, 2026, 07:29 AM

📷 Photo

📌 How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-27 | ⏱️ Read time: 10 min read

A warehouse picking operation is the process of collecting items from storage locations to fulfil…

#DataScience #AI #Python

1,090

Machine Learning

Apr 2, 2026, 07:29 AM

📷 Photo

Classical filters & convolution: The heart of computer vision

Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.

More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

746

Machine Learning

Apr 2, 2026, 07:29 AM

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📌 Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-27 | ⏱️ Read time: 14 min read

A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups…

#DataScience #AI #Python

917

Machine Learning

Mar 27, 2026, 03:32 PM

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📌 The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read

How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…

#DataScience #AI #Python

807

Machine Learning

Mar 27, 2026, 03:32 PM

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📌 From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read

How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making

#DataScience #AI #Python

702

Machine Learning

Mar 27, 2026, 03:32 PM

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📌 Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read

We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…

#DataScience #AI #Python

782

Machine Learning

Mar 27, 2026, 03:32 PM

🎥 Video

Visualizing the complexity of algorithms

https://t.me/CodeProgrammer ✅

529

Machine Learning

Mar 27, 2026, 03:32 PM

📷 Photo

📌 How to Make Claude Code Improve from its Own Mistakes

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read

Supercharge Claude Code with continual learning

#DataScience #AI #Python

685

Machine Learning

Mar 27, 2026, 03:32 PM

🎥 Video

💾 LLM Architecture Cheat Sheet: from GPT-2 to Trillion-scale Models

LLM Architecture Gallery — a page with cards for 39 models (2019–2026): DeepSeek, Qwen, Llama, Kimi, Grok, Nemotron, and others. For each — an architecture diagram, decoder type (dense / sparse MoE / hybrid), attention type, and links to technical reports and configs from HuggingFace.

It's clear how the market has converged on MoE + MLA for large models and why hybrid architectures (Mamba-2, DeltaNet, Lightning Attention) are gaining momentum.

🔘 Open Gallery
https://sebastianraschka.com/llm-architecture-gallery/

https://t.me/DataScienceT 🔴

697

Machine Learning

Mar 27, 2026, 03:32 PM

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🗂 Cheat sheet on neural networks

It clearly presents all the main types of Neural Networks, with a brief theory and useful tips on Python for working with data and machine learning.

Essentially, it's a compilation of various cheat sheets in one convenient document.

▶️ Link to the cheat sheet
https://www.bigdataheaven.com/wp-content/uploads/2019/02/AI-Neural-Networks.-22.pdf

751

Machine Learning

Mar 27, 2026, 03:32 PM

📷 Photo

📌 Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free)

🗂 Category: DEEP LEARNING

🕒 Date: 2026-03-23 | ⏱️ Read time: 24 min read

This Article asks what happens next. The model has encoded its knowledge of fraud as…

#DataScience #AI #Python

688

Machine Learning

Mar 27, 2026, 03:32 PM

📷 Photo

📌 Causal Inference Is Eating Machine Learning

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-23 | ⏱️ Read time: 14 min read

Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison…

#DataScience #AI #Python

702

Machine Learning

Mar 27, 2026, 03:32 PM

📷 Photo

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

283

Machine Learning

Mar 25, 2026, 12:39 PM

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.

Vision Transformers threw that whole approach out.

ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.

That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:

- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification

The Image below shows

Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.

The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.

Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.

The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.

𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬
ViT paper dissection
https://youtube.com/watch?v=U_sdodhcBC4

Build ViT from Scratch
https://youtube.com/watch?v=ZRo74xnN2SI

Original Paper
https://arxiv.org/abs/2010.11929

https://t.me/CodeProgrammer

Machine Learning

Mar 25, 2026, 12:39 PM

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📌 4 Pandas Concepts That Quietly Break Your Data Pipelines

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-23 | ⏱️ Read time: 10 min read

Master data types, index alignment, and defensive Pandas practices to prevent silent bugs in real…

#DataScience #AI #Python

460

Machine Learning

Mar 25, 2026, 12:39 PM

📷 Photo

📌 I Built a Podcast Clipping App in One Weekend Using Vibe Coding

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-23 | ⏱️ Read time: 12 min read

Rapid prototyping with Replit, AI agents, and minimal manual coding

#DataScience #AI #Python

650

Machine Learning

Mar 25, 2026, 12:39 PM

📷 Photo

📌 Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow

🗂 Category: PHYSICS

🕒 Date: 2026-03-22 | ⏱️ Read time: 6 min read

A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a…

#DataScience #AI #Python

788

Machine Learning

Mar 25, 2026, 12:39 PM

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🎁 23 Years of SPOTO – Claim Your Free IT Certs Prep Kit!

🔥Whether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!

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👉 Become Part of Our IT Learning Circle! resources and support:
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💬 Want exam help? Chat with an admin now!
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414

Machine Learning

Mar 25, 2026, 12:39 PM

🐍 PyTorch for Beginners: All the Basics on Tensors in One Place

A collection of basic techniques for working with tensors in PyTorch — for those who are starting to get acquainted with the framework and want to quickly master its fundamentals.

What's inside:
▶️ What tensors are and why they are needed

▶️ Tensor initialization: zeros, ones, random, similar size

▶️ Type conversion and switching between NumPy and PyTorch

▶️ Arithmetic, logical operations, tensor comparison

▶️ Matrix multiplication and batch computations

▶️ Broadcasting, view(), reshape(), changing dimensions

▶️ Indexing and slicing: how to access parts of a tensor

▶️ Notebook with code examples
A good starting material to understand the mechanics of tensors before moving on to models and training.

⛓ https://github.com/analyticalrohit/pytorch_fundamentals

tags: #useful

➡ https://t.me/codeprogrammer

889

Machine Learning

Mar 25, 2026, 12:39 PM

📷 Photo

📌 Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-03-22 | ⏱️ Read time: 9 min read

A step-by-step guide to making your OpenAI apps faster, cheaper, and more efficient

#DataScience #AI #Python

857

Machine Learning

Mar 25, 2026, 12:55 AM

📷 Photo

📌 Escaping the SQL Jungle

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-21 | ⏱️ Read time: 13 min read

Most data platforms don’t break overnight; they grow into complexity, query by query. Over time,…

#DataScience #AI #Python

860

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