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
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πŸ“Œ Combining Large and Small LLMs to Boost Inference Time and Quality

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-12-05 | ⏱️ Read time: 8 min read

Implementing Speculative and Contrastive Decoding
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πŸ“Œ SQL vs. Calculators: Building Champion/Challenger Tests from Scratch

πŸ—‚ Category: ANALYTICS

πŸ•’ Date: 2024-12-04 | ⏱️ Read time: 14 min read

In depth SQL code for creating your own statistical test design
πŸ“Œ TDS Newsletter: How to Make Smarter Business Decisions with AI

πŸ—‚ Category: THE VARIABLE

πŸ•’ Date: 2025-09-18 | ⏱️ Read time: 3 min read

Research agents, budget planners, and more
πŸ“Œ TDS Newsletter: What You Need to Know About LLMs for Data Science

πŸ—‚ Category: THE VARIABLE

πŸ•’ Date: 2025-09-11 | ⏱️ Read time: 3 min read

How new tools are transforming data scientists’ essential workflows
πŸ“Œ The Arcane Network

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 8 min read

How to use network science and Python to map out the popular show
πŸ“Œ From Code to Paper: Using GPT Models and Python to Generate Scientific LaTeX Documents

πŸ—‚ Category: CHATGPT

πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 13 min read

Automating Scientific Code Documentation: A GPT-Powered POC for Streamlined Workflows
πŸ“Œ Google Gemini Is Entering the Advent of Code Challenge

πŸ—‚ Category: CHATGPT

πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 8 min read

An open-source project to explore the capabilities and limitations of LLMs on coding challenges
πŸ“Œ Modeling DAU with Markov Chain

πŸ—‚ Category:

πŸ•’ Date: 2024-12-02 | ⏱️ Read time: 23 min read

How to predict DAU using Duolingo’s growth model and control the prediction
πŸ“Œ Smaller Is Smarter

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-12-01 | ⏱️ Read time: 4 min read

Do you really need the power of top LLMs for a tiramisu recipe in Shakespearean…
πŸ“Œ The Lead, Shadow, and Sparring Roles in New Data Settings

πŸ—‚ Category:

πŸ•’ Date: 2024-12-01 | ⏱️ Read time: 10 min read

From data engineer to domain expert-what it takes to build a new data platform
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πŸ“Œ How to Solve a Simple Problem With Machine Learning

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-12-01 | ⏱️ Read time: 8 min read

A technical walkthrough of lesson one
πŸ“Œ Making News Recommendations Explainable with Large Language Models

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-11-30 | ⏱️ Read time: 7 min read

A prompt-based experiment to improve both accuracy and transparent reasoning in content personalization.
πŸ“Œ Grokking Behavioral Interviews

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-30 | ⏱️ Read time: 8 min read

Master the art of behavioral interviews and land your dream job
πŸ“Œ Model Validation Techniques, Explained: A Visual Guide with Code Examples

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-11-30 | ⏱️ Read time: 28 min read

12 must-know methods to validate your machine learning
πŸ“Œ Dunder Methods: The Hidden Gems of Python

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-30 | ⏱️ Read time: 9 min read

Real-world examples on how actively using special methods can simplify coding and improve readability
πŸ“Œ Think you Know Excel? Take Your Analytics Skills to the Next Level with Power Query!

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-29 | ⏱️ Read time: 8 min read

5 practical use cases that prove Power Query is worth exploring.
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πŸ“Œ Water Cooler Small Talk: Simpson’s Paradox

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-29 | ⏱️ Read time: 10 min read

Is your data tricking you? What can you do about it?
πŸ“Œ Multimodal Embeddings: An Introduction

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-29 | ⏱️ Read time: 8 min read

Mapping text and images into a common space
πŸ“Œ AI Math: The Bias-Variance Trade-off in Deep Learning

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2024-11-29 | ⏱️ Read time: 60 min read

A visual tour from classical statistics to the nuances of deep learning