In this article, you will learn three reliable techniques — ordinal encoding, one-hot encoding, and target (mean) encoding — for turning categorical features into model-ready numbers while preserving their meaning.
Author Archive | Shittu Olumide
How to Fine-Tune a Local Mistral or Llama 3 Model on Your Own Dataset
Learn how to perform the same fine-tuning method on 2 different models to better compare and understand the process, and confidently move on to other models yourself.
3 Feature Engineering Techniques for Unstructured Text Data
In this article, you will learn practical ways to convert raw text into numerical features that machine learning models can use, ranging from statistical counts to semantic and contextual embeddings.
3 Ways to Speed Up Model Training Without More GPUs
In this article, we’ll look at three practical methods that consistently boost training performance without upgrading your hardware.
10 Python One-Liners for Calling LLMs from Your Code
In this article, you will learn how to call popular large language models from Python using concise one-liners for both hosted APIs and local servers.
Beyond Pandas: 7 Advanced Data Manipulation Techniques for Large Datasets
We’re going to look at seven tools and techniques that go beyond Pandas, things built for bigger data, faster execution, and more efficient pipelines.
10 Underrated Books for Mastering Machine Learning
Unlock 10 hidden gems in machine learning — essential reads covering core theories, applications, and recent advancements for data scientists.
Machine Learning Salaries and Job Market Analysis for 2024 and Beyond
One of the most talked-about niches in tech is machine learning (ML), as developments in this area are expected to have a significant impact on IT as well as other industries. The field has grown at an extraordinary pace, revolutionizing several industries along the way. As companies increasingly integrate AI-driven solutions into their operations, the […]