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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πŸ“Œ Exploratory Data Analysis for Credit Scoring with Python

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-12 | ⏱️ Read time: 16 min read

Understanding default risk through statistical analysis of borrower and loan characteristics.

#DataScience #AI #Python
Machine Learning in python.pdf
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Machine Learning in Python (Course Notes)

I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!

Here’s what you’ll learn:

πŸ”˜ Linear Regression - The foundation of predictive modeling

πŸ”˜ Logistic Regression - Predicting probabilities and classifications

πŸ”˜ Clustering (K-Means, Hierarchical) - Making sense of unstructured data

πŸ”˜ Overfitting vs. Underfitting - The balancing act every ML engineer must master

πŸ”˜ OLS, R-squared, F-test - Key metrics to evaluate your models

https://t.me/CodeProgrammer || Share 🌐 and Like πŸ‘
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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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πŸ“Œ Solving the Human Training Data Problem

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-03-12 | ⏱️ Read time: 18 min read

How AI has completely transformed the way I study as a graduate student

#DataScience #AI #Python
πŸ“Œ Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-03-12 | ⏱️ Read time: 11 min read

Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs…

#DataScience #AI #Python
πŸ“Œ I Finally Built My First AI App (And It Wasn’t What I Expected)

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-03-12 | ⏱️ Read time: 14 min read

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

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πŸ“Œ A Tale of Two Variances: Why NumPy and Pandas Give Different Answers

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-13 | ⏱️ Read time: 7 min read

Imagine you are analyzing a small dataset: You want to calculate some summary statistics to…

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

πŸ—‚ Category: RAG

πŸ•’ Date: 2026-03-13 | ⏱️ Read time: 7 min read

Learn how to build a powerful agentic RAG system

#DataScience #AI #Python
πŸ—‚ Building our own mini-Skynet β€” 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 β€” this is an excellent starting kit.

tags: #github #LLM #AI #ML

➑️ https://t.me/CodeProgrammer
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πŸ“Œ Why Care About Prompt Caching in LLMs?

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-03-13 | ⏱️ Read time: 11 min read

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

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πŸ“Œ How Vision Language Models Are Trained from β€œScratch”

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-03-13 | ⏱️ Read time: 13 min read

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

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πŸ“Œ Personalized Restaurant Ranking with a Two-Tower Embedding Variant

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-03-13 | ⏱️ Read time: 6 min read

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

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πŸ“Œ The Multi-Agent Trap

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-03-14 | ⏱️ Read time: 12 min read

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

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

πŸ—‚ Category: QUANTUM COMPUTING

πŸ•’ Date: 2026-03-14 | ⏱️ Read time: 8 min read

How do we program quantum computers today?

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πŸ“Œ The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability?

πŸ—‚ Category: DATA GOVERNANCE

πŸ•’ Date: 2026-03-15 | ⏱️ Read time: 8 min read

Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loop…

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πŸ“Œ The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-15 | ⏱️ Read time: 17 min read

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

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πŸ“Œ Bayesian Thinking for People Who Hated Statistics

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-16 | ⏱️ Read time: 12 min read

You already think like a Bayesian. Your stats class just taught the formula before the…

#DataScience #AI #Python
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