Machine Learning
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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
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๐Ÿ“Œ I Finally Built My First AI App (And It Wasnโ€™t What I Expected)

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-12 | โฑ๏ธ Read time: 14 min read

A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure

#DataScience #AI #Python
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๐Ÿ“Œ A Tale of Two Variances: Why NumPy and Pandas Give Different Answers

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-13 | โฑ๏ธ Read time: 7 min read

Imagine you are analyzing a small dataset: You want to calculate some summary statistics toโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ How to Build Agentic RAG with Hybrid Search

๐Ÿ—‚ Category: RAG

๐Ÿ•’ Date: 2026-03-13 | โฑ๏ธ Read time: 7 min read

Learn how to build a powerful agentic RAG system

#DataScience #AI #Python
๐Ÿ—‚ Building our own mini-Skynet โ€” a collection of 10 powerful AI repositories from big tech companies

1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.

2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".

3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.

4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.

5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.

6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.

If you want to delve deeply into AI or start building your own projects โ€” this is an excellent starting kit.

tags: #github #LLM #AI #ML

โžก๏ธ https://t.me/CodeProgrammer
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๐Ÿ“Œ Why Care About Prompt Caching in LLMs?

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-13 | โฑ๏ธ Read time: 11 min read

Optimizing the cost and latency of your LLM calls with Prompt Caching

#DataScience #AI #Python
๐Ÿ“Œ How Vision Language Models Are Trained from โ€œScratchโ€

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-13 | โฑ๏ธ Read time: 13 min read

A deep dive into exactly how text-only language models are finetuned to see images

#DataScience #AI #Python
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๐Ÿ“Œ Personalized Restaurant Ranking with a Two-Tower Embedding Variant

๐Ÿ—‚ Category: MACHINE LEARNING

๐Ÿ•’ Date: 2026-03-13 | โฑ๏ธ Read time: 6 min read

How a lightweight two-tower model improved restaurant discovery when popularity ranking failed

#DataScience #AI #Python
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๐Ÿ“Œ The Multi-Agent Trap

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-14 | โฑ๏ธ Read time: 12 min read

Google DeepMind found multi-agent networks amplify errors 17x. Learn 3 architecture patterns that separate $60Mโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ The Current Status of The Quantum Software Stack

๐Ÿ—‚ Category: QUANTUM COMPUTING

๐Ÿ•’ Date: 2026-03-14 | โฑ๏ธ Read time: 8 min read

How do we program quantum computers today?

#DataScience #AI #Python
๐Ÿ“Œ The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability?

๐Ÿ—‚ Category: DATA GOVERNANCE

๐Ÿ•’ Date: 2026-03-15 | โฑ๏ธ Read time: 8 min read

Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loopโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-15 | โฑ๏ธ Read time: 17 min read

Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity,โ€ฆ

#DataScience #AI #Python
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๐Ÿ“Œ Bayesian Thinking for People Who Hated Statistics

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 12 min read

You already think like a Bayesian. Your stats class just taught the formula before theโ€ฆ

#DataScience #AI #Python
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๐Ÿ“Œ Hallucinations in LLMs Are Not a Bug in the Data

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 10 min read

Itโ€™s a feature of the architecture

#DataScience #AI #Python
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๐Ÿ“Œ Follow the AI Footpaths

๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE

๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 6 min read

Shadow AI and the desire paths of modern work

#DataScience #AI #Python
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๐Ÿ“Œ How to Build a Production-Ready Claude Code Skill

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 11 min read

What I learned building and distributing my first Skill from scratch

#DataScience #AI #Python
๐Ÿ“Œ Introducing Gemini Embeddings 2 Preview

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 10 min read

One embedding model to rule them all

#DataScience #AI #Python
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๐Ÿ“Œ How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment

๐Ÿ—‚ Category: DEEP LEARNING

๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 18 min read

Most neuro-symbolic systems inject rules written by humans. But what if a neural network couldโ€ฆ

#DataScience #AI #Python
TOP RAG INTERVIEW.pdf
166 KB
๐Ÿš€ ๐“๐Ž๐ ๐‘๐€๐† ๐ˆ๐๐“๐„๐‘๐•๐ˆ๐„๐– ๐๐”๐„๐’๐“๐ˆ๐Ž๐๐’ ๐€๐๐ƒ ๐€๐๐’๐–๐„๐‘๐’ โฃโฃ

๐Ÿ”น Advanced #RAG engineering conceptsโฃโฃ
โ€ข Multi-stage retrieval pipelinesโฃโฃ
โ€ข Agentic RAG vs classical RAGโฃโฃ
โ€ข Latency optimizationโฃโฃ
โ€ข Security risks in enterprise RAG systemsโฃโฃ
โ€ข Monitoring and debugging production RAG systemsโฃโฃ
โฃโฃ
๐Ÿ“„ ๐“๐ก๐ž ๐๐ƒ๐… ๐œ๐จ๐ง๐ญ๐š๐ข๐ง๐ฌ ๐Ÿ’๐ŸŽ ๐ฌ๐ญ๐ซ๐ฎ๐œ๐ญ๐ฎ๐ซ๐ž๐ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ฐ๐ข๐ญ๐ก ๐œ๐ฅ๐ž๐š๐ซ ๐ž๐ฑ๐ฉ๐ฅ๐š๐ง๐š๐ญ๐ข๐จ๐ง๐ฌ ๐ญ๐จ ๐ก๐ž๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐ฎ๐ง๐๐ž๐ซ๐ฌ๐ญ๐š๐ง๐ ๐›๐จ๐ญ๐ก ๐œ๐จ๐ง๐œ๐ž๐ฉ๐ญ๐ฌ ๐š๐ง๐ ๐ฌ๐ฒ๐ฌ๐ญ๐ž๐ฆ ๐๐ž๐ฌ๐ข๐ ๐ง ๐ญ๐ก๐ข๐ง๐ค๐ข๐ง๐ .โฃโฃ
โฃโฃ
https://t.me/CodeProgrammer
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