Machine Learning Mastery

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 ...

10 Must-Know Python Libraries for Machine Learning in 2025

Python is one of the most popular languages for machine learning, and it’s easy ...

Understanding Text Generation Parameters in Transformers

This post is divided into seven parts; they are: - Core Text Generation Paramete...

Further Applications with Context Vectors

This post is divided into three parts; they are: • Building a Semantic Search En...

Zero-Shot and Few-Shot Learning with Reasoning LLMs

As large language models have already become essential components of so many rea...

Automate Dataset Labeling with Active Learning

A few years ago, training AI models required massive amounts of labeled data.

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