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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.

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TOP RAG INTERVIEW.pdf
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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
โค2
Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For โณ๐Ÿง 

https://t.me/DataScienceM
โค3
๐Ÿ“Œ How to Effectively Review Claude Code Output

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 7 min read

Get more out of your coding agents by making reviewing more efficient

#DataScience #AI #Python
โค2
๐Ÿ“Œ Self-Hosting Your First LLM

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 20 min read

Privacy. Cost. Customization. Everything you need to knowโ€”step by step.

#DataScience #AI #Python
CNN vs Vision Transformer โ€” The Battle for Computer Vision ๐Ÿ‘โšก๏ธ

Two architectures. One goal: identify the cat. But they see things differently:

๐Ÿง  CNN (Convolutional Neural Network)

ยท Scans the image with filters
ยท Detects local patterns first (edges โ†’ textures โ†’ shapes)
ยท Builds understanding layer by layer

๐Ÿ”„ Vision Transformer (ViT)

ยท Splits image into patches (like words in a sentence)
ยท Detects global patterns from the start
ยท Sees the whole picture using attention mechanisms

Same input. Same output. Different journey.

CNNs think locally and build up.
Transformers think globally from the get-go.

Which one wins? Depends on the task โ€” but both are shaping the future of how machines see.

https://t.me/CodeProgrammer
โค2
๐Ÿ“Œ Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes

๐Ÿ—‚ Category: MACHINE LEARNING

๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 20 min read

Why one model canโ€™t do two jobs

#DataScience #AI #Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐Ÿ“Œ The New Experience of Coding with AI

๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE

๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 12 min read

The seduction of AI code assistants

#DataScience #AI #Python
๐Ÿ“Œ Why You Should Stop Worrying About AI Taking Data Science Jobs

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 8 min read

Itโ€™s all just fearmongering

#DataScience #AI #Python
PhD Students - Do you need datasets for your research?

Here are 30 datasets for research from NexData.

Use discount code for 20% off: G5W924C3ZI

1. Korean Exam Question Dataset for AI Training

https://lnkd.in/d_paSwt7

2. Multilingual Grammar Correction Dataset

https://lnkd.in/dV43iqTp

3. High quality video caption dataset

https://lnkd.in/dY9kxkhx

4. 3D models and scenes datasets for AI and simulation

https://lnkd.in/dT-zscH4

5. Image editing datasets โ€“ object removal, addition & modification

https://lnkd.in/dd8iCGMS

6. QA dataset โ€“ visual & text reasoning

https://lnkd.in/dc3TNWFD

7. English instruction tuning dataset

https://lnkd.in/dTeTgd2M

8. Large scale vision language dataset for AI training

https://lnkd.in/dBJuxazN

9. News dataset

https://lnkd.in/dYBJe5gd

10. Global building photos dataset

https://lnkd.in/dVJsDXnC

11. Facial landmarks dataset

https://lnkd.in/dz_KGCS4

12. 3D Human Pose & Landmarks dataset

https://lnkd.in/dXE9ir8Z

13. 3D Hand Pose & Gesture Recognition dataset

https://lnkd.in/d_QdGGb9

14. 14. Driver monitoring dataset โ€“ dangerous, fatigue

https://lnkd.in/d6kF-9PW

15. Japanese handwriting OCR dataset

https://lnkd.in/dHnriqrH

16. American English Male voice TTS dataset

https://lnkd.in/dqyvg862

17. Riddles and brain teasers dataset

https://lnkd.in/dKBHY3DE

18. Chinese test questions text

https://lnkd.in/dQpUd8xC

19. Chinese medical question answering data

https://lnkd.in/dsbWUCpz

20. Multi-round interpersonal dialogues text data

https://lnkd.in/dQiUq_Jg

21. Human activity recognition dataset

https://lnkd.in/dHM52MfV

22. Facial expression recognition dataset

https://lnkd.in/dqQAfMau

23. Urban surveillance dataset

https://lnkd.in/dc2RCnTk

24. Human body segmentation dataset

https://lnkd.in/d6sSrDxS

25. Fashion segmentation โ€“ clothing & accessories

https://lnkd.in/dptNUTz8

26. Fight video dataset โ€“ action recognition

https://lnkd.in/dnY_m5hZ

27. Gesture recognition dataset

https://lnkd.in/dFVPivYg

28. Facial skin defects dataset

https://lnkd.in/dKCbUvU6

29. Smoke detection and behaviour recognition dataset

https://lnkd.in/ddGg56R4

30. Weight loss transformation video dataset

https://lnkd.in/dqqT4ed9

https://t.me/CodeProgrammer ๐Ÿ‘พ
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โค4
๐Ÿ“Œ Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 13 min read

A practical guide to caching layers across the RAG pipeline, from query embeddings to fullโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 14 min read

A visual guide to vectors and projections

#DataScience #AI #Python
โค1
๐Ÿ“Œ Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 16 min read

Accelerate coding with AI while staying in control and building reliable, production-ready software.

#DataScience #AI #Python
๐Ÿ“Œ The Basics of Vibe Engineering

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 14 min read

Building products without the coding part

#DataScience #AI #Python
โค1
๐Ÿ“Œ Building Robust Credit Scoring Models (Part 3)

๐Ÿ—‚ Category: MACHINE LEARNING

๐Ÿ•’ Date: 2026-03-20 | โฑ๏ธ Read time: 18 min read

Handling outliers and missing values in borrower data using Python.

#DataScience #AI #Python
โค2
๐Ÿ“Œ How to Measure AI Value

๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE

๐Ÿ•’ Date: 2026-03-20 | โฑ๏ธ Read time: 12 min read

While efficiency is an important source of AI value, it is only part of theโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ Agentic RAG Failure Modes: Retrieval Thrash, Tool Storms, and Context Bloat (and How to Spot Them Early)

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-03-20 | โฑ๏ธ Read time: 8 min read

Why agentic RAG systems fail silently in production and how to detect them before yourโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ The Math Thatโ€™s Killing Your AI Agent

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-03-20 | โฑ๏ธ Read time: 12 min read

An 85% accurate AI agent fails 4 out of 5 times on a 10-step task.โ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ Escaping the SQL Jungle

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-21 | โฑ๏ธ Read time: 13 min read

Most data platforms donโ€™t break overnight; they grow into complexity, query by query. Over time,โ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-03-21 | โฑ๏ธ Read time: 21 min read

Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP solversโ€ฆ

#DataScience #AI #Python
๐Ÿ PyTorch for Beginners: All the Basics on Tensors in One Place

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

What's inside:
โ–ถ๏ธ What tensors are and why they are needed

โ–ถ๏ธ Tensor initialization: zeros, ones, random, similar size

โ–ถ๏ธ Type conversion and switching between NumPy and PyTorch

โ–ถ๏ธ Arithmetic, logical operations, tensor comparison

โ–ถ๏ธ Matrix multiplication and batch computations

โ–ถ๏ธ Broadcasting, view(), reshape(), changing dimensions

โ–ถ๏ธ Indexing and slicing: how to access parts of a tensor

โ–ถ๏ธ Notebook with code examples
A good starting material to understand the mechanics of tensors before moving on to models and training.

โ›“ GitHub link

tags: #useful

โžก @codeprogrammer
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