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@DataScienceM
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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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Machine Learning
39.6K subscribers
Machine Learning
4 learning paradigms in machine learning, explained visually:

1. Transfer Learning
2. Fine-tuning
3. Multi-task Learning
4. Federated Learning

๐Ÿ‘‰ @DataScienceM
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Machine Learning
๐Ÿ“Œ How to Leverage Slash Commands to Code Effectively

๐Ÿ—‚ Category: LLM APPLICATIONS

๐Ÿ•’ Date: 2026-01-11 | โฑ๏ธ Read time: 8 min read

Learn how I utilize slash commands to be a more efficient engineer

#DataScience #AI #Python
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Forwarded from Machine Learning with Python
๐Š_๐๐ž๐š๐ซ๐ž๐ฌ๐ญ_๐๐ž๐ข๐ ๐ก๐›๐จ๐ซ๐ฌ_๐Š๐๐โฃ.pdf
2.4 MB
๐Ÿง  ๐Š-๐๐ž๐š๐ซ๐ž๐ฌ๐ญ ๐๐ž๐ข๐ ๐ก๐›๐จ๐ซ๐ฌ (๐Š๐๐)โฃ

๐Ÿ”น ๐–๐ก๐š๐ญ ๐ˆ ๐œ๐จ๐ฏ๐ž๐ซ๐ž๐ ๐ญ๐จ๐๐š๐ฒโฃ
๐–๐ก๐š๐ญ ๐Š๐๐ ๐ข๐ฌ ๐š๐ง๐ ๐ก๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌโฃ
๐ƒ๐ข๐Ÿ๐Ÿ๐ž๐ซ๐ž๐ง๐œ๐ž ๐›๐ž๐ญ๐ฐ๐ž๐ž๐ง ๐Š๐๐ ๐Ÿ๐จ๐ซ ๐‚๐ฅ๐š๐ฌ๐ฌ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง ๐ฏ๐ฌ ๐‘๐ž๐ ๐ซ๐ž๐ฌ๐ฌ๐ข๐จ๐งโฃ
๐‘๐จ๐ฅ๐ž ๐จ๐Ÿ ๐Š (๐ก๐ฒ๐ฉ๐ž๐ซ๐ฉ๐š๐ซ๐š๐ฆ๐ž๐ญ๐ž๐ซ)โฃ
๐ƒ๐ข๐ฌ๐ญ๐š๐ง๐œ๐ž ๐ฆ๐ž๐ญ๐ซ๐ข๐œ๐ฌ: ๐„๐ฎ๐œ๐ฅ๐ข๐๐ž๐š๐ง ๐ฏ๐ฌ ๐Œ๐š๐ง๐ก๐š๐ญ๐ญ๐š๐งโฃ
๐–๐ก๐ฒ ๐Š๐๐ ๐ข๐ฌ ๐œ๐š๐ฅ๐ฅ๐ž๐ ๐š ๐ฅ๐š๐ณ๐ฒ / ๐ข๐ง๐ฌ๐ญ๐š๐ง๐œ๐ž-๐›๐š๐ฌ๐ž๐ ๐ฅ๐ž๐š๐ซ๐ง๐ž๐ซโฃ
โฃ
๐ŸŽฏ ๐“๐จ๐ฉ ๐Ÿ๐ŸŽ ๐ˆ๐ง๐ญ๐ž๐ซ๐ฏ๐ข๐ž๐ฐ ๐๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐Œ๐ฎ๐ฌ๐ญ-๐Š๐ง๐จ๐ฐ)โฃ
โฃ
1๏ธโƒฃ ๐˜ž๐˜ฉ๐˜ข๐˜ต ๐˜ช๐˜ด ๐˜’-๐˜•๐˜ฆ๐˜ข๐˜ณ๐˜ฆ๐˜ด๐˜ต ๐˜•๐˜ฆ๐˜ช๐˜จ๐˜ฉ๐˜ฃ๐˜ฐ๐˜ณ๐˜ด (๐˜’๐˜•๐˜•)?โฃ
2๏ธโƒฃ ๐˜ž๐˜ฉ๐˜บ ๐˜ช๐˜ด ๐˜’๐˜•๐˜• ๐˜ค๐˜ข๐˜ญ๐˜ญ๐˜ฆ๐˜ฅ ๐˜ข ๐˜ญ๐˜ข๐˜ป๐˜บ ๐˜ญ๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ ๐˜ข๐˜ญ๐˜จ๐˜ฐ๐˜ณ๐˜ช๐˜ต๐˜ฉ๐˜ฎ?โฃ
3๏ธโƒฃ ๐˜‹๐˜ช๐˜ง๐˜ง๐˜ฆ๐˜ณ๐˜ฆ๐˜ฏ๐˜ค๐˜ฆ ๐˜ฃ๐˜ฆ๐˜ต๐˜ธ๐˜ฆ๐˜ฆ๐˜ฏ ๐˜’๐˜•๐˜• ๐˜ค๐˜ญ๐˜ข๐˜ด๐˜ด๐˜ช๐˜ง๐˜ช๐˜ค๐˜ข๐˜ต๐˜ช๐˜ฐ๐˜ฏ ๐˜ข๐˜ฏ๐˜ฅ ๐˜’๐˜•๐˜• ๐˜ณ๐˜ฆ๐˜จ๐˜ณ๐˜ฆ๐˜ด๐˜ด๐˜ช๐˜ฐ๐˜ฏ?โฃ
4๏ธโƒฃ ๐˜๐˜ฐ๐˜ธ ๐˜ฅ๐˜ฐ ๐˜บ๐˜ฐ๐˜ถ ๐˜ค๐˜ฉ๐˜ฐ๐˜ฐ๐˜ด๐˜ฆ ๐˜ต๐˜ฉ๐˜ฆ ๐˜ท๐˜ข๐˜ญ๐˜ถ๐˜ฆ ๐˜ฐ๐˜ง ๐˜’?โฃ
5๏ธโƒฃ ๐˜ž๐˜ฉ๐˜ข๐˜ต ๐˜ฉ๐˜ข๐˜ฑ๐˜ฑ๐˜ฆ๐˜ฏ๐˜ด ๐˜ธ๐˜ฉ๐˜ฆ๐˜ฏ ๐˜’ ๐˜ช๐˜ด ๐˜ต๐˜ฐ๐˜ฐ ๐˜ด๐˜ฎ๐˜ข๐˜ญ๐˜ญ ๐˜ฐ๐˜ณ ๐˜ต๐˜ฐ๐˜ฐ ๐˜ญ๐˜ข๐˜ณ๐˜จ๐˜ฆ?โฃ
6๏ธโƒฃ ๐˜ž๐˜ฉ๐˜ข๐˜ต ๐˜ฅ๐˜ช๐˜ด๐˜ต๐˜ข๐˜ฏ๐˜ค๐˜ฆ ๐˜ฎ๐˜ฆ๐˜ต๐˜ณ๐˜ช๐˜ค๐˜ด ๐˜ข๐˜ณ๐˜ฆ ๐˜ค๐˜ฐ๐˜ฎ๐˜ฎ๐˜ฐ๐˜ฏ๐˜ญ๐˜บ ๐˜ถ๐˜ด๐˜ฆ๐˜ฅ ๐˜ช๐˜ฏ ๐˜’๐˜•๐˜•?โฃ
7๏ธโƒฃ ๐˜ž๐˜ฉ๐˜บ ๐˜ฅ๐˜ฐ๐˜ฆ๐˜ด ๐˜’๐˜•๐˜• ๐˜ฑ๐˜ฆ๐˜ณ๐˜ง๐˜ฐ๐˜ณ๐˜ฎ ๐˜ฑ๐˜ฐ๐˜ฐ๐˜ณ๐˜ญ๐˜บ ๐˜ฐ๐˜ฏ ๐˜ฉ๐˜ช๐˜จ๐˜ฉ-๐˜ฅ๐˜ช๐˜ฎ๐˜ฆ๐˜ฏ๐˜ด๐˜ช๐˜ฐ๐˜ฏ๐˜ข๐˜ญ ๐˜ฅ๐˜ข๐˜ต๐˜ข?โฃ
8๏ธโƒฃ ๐˜ž๐˜ฉ๐˜ข๐˜ต ๐˜ช๐˜ด ๐˜ต๐˜ฉ๐˜ฆ ๐˜ต๐˜ช๐˜ฎ๐˜ฆ ๐˜ค๐˜ฐ๐˜ฎ๐˜ฑ๐˜ญ๐˜ฆ๐˜น๐˜ช๐˜ต๐˜บ ๐˜ฐ๐˜ง ๐˜’๐˜•๐˜•?โฃ
9๏ธโƒฃ ๐˜๐˜ฐ๐˜ธ ๐˜ฅ๐˜ฐ ๐˜’๐˜‹-๐˜›๐˜ณ๐˜ฆ๐˜ฆ ๐˜ข๐˜ฏ๐˜ฅ ๐˜‰๐˜ข๐˜ญ๐˜ญ-๐˜›๐˜ณ๐˜ฆ๐˜ฆ ๐˜ช๐˜ฎ๐˜ฑ๐˜ณ๐˜ฐ๐˜ท๐˜ฆ ๐˜’๐˜•๐˜• ๐˜ฑ๐˜ฆ๐˜ณ๐˜ง๐˜ฐ๐˜ณ๐˜ฎ๐˜ข๐˜ฏ๐˜ค๐˜ฆ?โฃ
๐Ÿ”Ÿ ๐˜ž๐˜ฉ๐˜ฆ๐˜ฏ ๐˜ด๐˜ฉ๐˜ฐ๐˜ถ๐˜ญ๐˜ฅ ๐˜บ๐˜ฐ๐˜ถ ๐˜ข๐˜ท๐˜ฐ๐˜ช๐˜ฅ ๐˜ถ๐˜ด๐˜ช๐˜ฏ๐˜จ #๐˜’๐˜•๐˜•?โฃ

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Machine Learning
๐Ÿ“Œ How AI Can Become Your Personal Language Tutor

๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE

๐Ÿ•’ Date: 2026-01-12 | โฑ๏ธ Read time: 11 min read

How I used n8n to build AI study partners for learning Mandarin: vocabulary, listening, andโ€ฆ

#DataScience #AI #Python
โค2
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Forwarded from Machine Learning with Python
These Google Colab-notebooks help to implement all machine learning algorithms from scratch ๐Ÿคฏ

Repo: https://udlbook.github.io/udlbook/


๐Ÿ‘‰ @codeprogrammer
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Machine Learning
๐Ÿ“Œ Why 90% Accuracy in Text-to-SQL is 100% Useless

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-01-12 | โฑ๏ธ Read time: 9 min read

The eternal promise of self-service analytics

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๐Ÿ“Œ When Does Adding Fancy RAG Features Work?

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-01-12 | โฑ๏ธ Read time: 23 min read

Looking at the performance of different pipelines

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๐Ÿ“Œ Optimizing Data Transfer in Batched AI/ML Inference Workloads

๐Ÿ—‚ Category: DATA ENGINEERING

๐Ÿ•’ Date: 2026-01-12 | โฑ๏ธ Read time: 13 min read

A deep dive on data transfer bottlenecks, their identification, and their resolution with the helpโ€ฆ

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๐Ÿ“Œ Why Your ML Model Works in Training But Fails in Production

๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE

๐Ÿ•’ Date: 2026-01-13 | โฑ๏ธ Read time: 8 min read

Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, andโ€ฆ

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โค3
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๐Ÿ“Œ How to Maximize Claude Code Effectiveness

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-01-13 | โฑ๏ธ Read time: 9 min read

Learn how to get the most out of agentic coding

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โค2
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Forwarded from Machine Learning with Python
โšก๏ธ All cheat sheets for programmers in one place.

There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.

No registration required and it's free.

https://overapi.com/

#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS

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โค4
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Machine Learning
๐Ÿ“Œ An introduction to AWS Bedrock

๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE

๐Ÿ•’ Date: 2026-01-13 | โฑ๏ธ Read time: 13 min read

The how, why, what and where of Amazonโ€™s LLM access layer

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๐Ÿ“Œ From โ€˜Dataslowsโ€™ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric

๐Ÿ—‚ Category: DATA ENGINEERING

๐Ÿ•’ Date: 2026-01-13 | โฑ๏ธ Read time: 8 min read

Dataflows were (rightly?) considered โ€œthe slowest and least performant optionโ€ for ingesting data into Powerโ€ฆ

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