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 How to Call Rust from Python

๐Ÿ—‚ Category: PROGRAMMING

๐Ÿ•’ Date: 2026-04-21 | โฑ๏ธ Read time: 10 min read

A guide to bridging the gap between ease of use and raw performance.

#DataScience #AI #Python
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๐Ÿ”ฅ Google Colab has added the option of retraining 500+ open-source neural networks

Unsloth has released a convenient notebook for configuring models.

Instructions:

1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb

2. Run the blocks and the Unsloth Studio itself.

3. Select a model and a dataset.

4. Click "Start Training" and monitor the progress in real time.

5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
๐Ÿ“Œ I Replaced GPT-4 with a Local SLM and My CI/CD Pipeline Stopped Failing

๐Ÿ—‚ Category: MACHINE LEARNING

๐Ÿ•’ Date: 2026-04-21 | โฑ๏ธ Read time: 13 min read

The hidden cost of probabilistic outputs in systems that demand reliability

#DataScience #AI #Python
๐Ÿ“Œ Your RAG Gets Confidently Wrong as Memory Grows โ€“ I Built the Memory Layer That Stops It

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-04-21 | โฑ๏ธ Read time: 15 min read

As memory grows in RAG systems, accuracy quietly drops while confidence rises โ€” creating aโ€ฆ

#DataScience #AI #Python
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๐Ÿ“Œ Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-04-22 | โฑ๏ธ Read time: 19 min read

Turning free-to-use data into a hypothesis-ready dataset

#DataScience #AI #Python
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๐Ÿ“Œ Correlation vs. Causation: Measuring True Impact with Propensity Score Matching

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-04-22 | โฑ๏ธ Read time: 12 min read

Learn how Propensity Score Matching uncovers true causality in observational data. By finding โ€œstatistical twins,โ€โ€ฆ

#DataScience #AI #Python
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11 Plots Data Scientists Use 90% of the Time ๐Ÿ“Š๐Ÿš€

Hereโ€™s the secret โ†’ Data scientists donโ€™t actually use 100+ types of charts. ๐Ÿคซ

When real decisions are on the line, it always comes back to the same 11.

https://t.me/DataScienceM
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๐Ÿ“Œ From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-04-22 | โฑ๏ธ Read time: 8 min read

How I turned LLM persona interviews into a repeatable customer research workflow

#DataScience #AI #Python
๐Ÿ“Œ Ivory Tower Notes: The Methodology

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-04-22 | โฑ๏ธ Read time: 6 min read

A short intro to scientific methodology to combat โ€œprompt in, slop outโ€

#DataScience #AI #Python
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Today, the public mint for Lobsters on TON goes live on Getgems ๐Ÿฆž

This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.

Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.

So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.

What makes this especially interesting is the timing.

In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint โ€” it feels like a very precise fit for the new narrative:

Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility

Put simply, this is one of the first real attempts to turn an NFT from โ€œjust an imageโ€ into a digital agent.

Public mint: today, 16:00
Price: 50 TON

๐Ÿ‘‰ Mint your Lobster on Getgems ๐Ÿฆž๐Ÿฆž๐Ÿฆž
๐Ÿ“Œ How to Run OpenClaw with Open-Source Models

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-04-22 | โฑ๏ธ Read time: 8 min read

Run OpenClaw assistant through alternative LLMs

#DataScience #AI #Python
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๐Ÿ“Œ Using a Local LLM as a Zero-Shot Classifier

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-04-23 | โฑ๏ธ Read time: 8 min read

A practical pipeline for classifying messy free-text data into meaningful categories using a locally hostedโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ I Simulated an International Supply Chain and Let OpenClaw Monitor It

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2026-04-23 | โฑ๏ธ Read time: 9 min read

Mario asked me why 18% of his shipments were late when every team hit theirโ€ฆ

#DataScience #AI #Python
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๐Ÿ“Œ Your Synthetic Data Passed Every Test and Still Broke Your Model

๐Ÿ—‚ Category: DATA SCIENCE

๐Ÿ•’ Date: 2026-04-23 | โฑ๏ธ Read time: 11 min read

The silent gaps in synthetic data that only show up when your model is alreadyโ€ฆ

#DataScience #AI #Python
๐Ÿ“Œ Lasso Regression: Why the Solution Lives on a Diamond

๐Ÿ—‚ Category: MACHINE LEARNING

๐Ÿ•’ Date: 2026-04-23 | โฑ๏ธ Read time: 24 min read

Itโ€™s simpler than you think.

#DataScience #AI #Python
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๐Ÿ“Œ Introduction to Approximate Solution Methods for Reinforcement Learning

๐Ÿ—‚ Category: MACHINE LEARNING

๐Ÿ•’ Date: 2026-04-24 | โฑ๏ธ Read time: 9 min read

Learn about function approximation and the different choices for approximation functions

#DataScience #AI #Python
๐Ÿ“Œ I Built an AI Pipeline for Kindle Highlights

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2026-04-24 | โฑ๏ธ Read time: 12 min read

A local, zero-cost project that cleans, structures, and summarizes your reading automatically

#DataScience #AI #Python