Machine Learning Mastery

How to Combine Scikit-learn, CatBoost, and SHAP for Exp...

Machine learning workflows often involve a delicate balance: you want models tha...

10 Must-Know Python Libraries for MLOps in 2025

MLOps, or machine learning operations, is all about managing the end-to-end proc...

7 Concepts Behind Large Language Models Explained in 7 ...

If you've been using large language models like GPT-4 or Claude, you've probably...

Interpolation in Positional Encodings and Using YaRN fo...

This post is divided into three parts; they are: • Interpolation and Extrapolati...

10 Python One-Liners That Will Simplify Feature Enginee...

Feature engineering is a key process in most data analysis workflows, especially...

Word Embeddings in Language Models

This post is divided into three parts; they are: • Understanding Word Embeddings...

A Gentle Introduction to SHAP for Tree-Based Models

Machine learning models have become increasingly sophisticated, but this complex...

Using Quantized Models with Ollama for Application Deve...

Quantization is a frequently used strategy applied to production machine learnin...

Tokenizers in Language Models

This post is divided into five parts; they are: • Naive Tokenization • Stemming ...

A Gentle Introduction to Learning Rate Schedulers

Ever wondered why your neural network seems to get stuck during training, or why...

Custom Fine-Tuning for Domain-Specific LLMs

Fine-tuning a large language model (LLM) is the process of taking a pre-trained ...

Roadmap to Python in 2025

Python has evolved from a simple scripting language to the backbone of modern da...

How to Combine Pandas, NumPy, and Scikit-learn Seamlessly

Machine learning workflows require several distinct steps — from loading and pre...

Let’s Build a RAG-Powered Research Paper Assistant

In the era of generative AI, people have relied on LLM products such as ChatGPT ...

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