Advanced Feature Engineering Using Scikit-Learn Pipelines with Pandas’ ColumnTransformer and NumPy Arrays
Pandas , NumPy , and Scikit-learn .
Navigating Imbalanced Datasets with Pandas and Scikit-learn
Imbalanced datasets, where a majority of the data samples belong to one class and the remaining minority belong to others, are not that rare.
Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
You’ve trained your machine learning model, and it’s performing great on test data.
Implementing Vector Search from Scratch: A Step-by-Step Tutorial
There’s no doubt that search is one of the most fundamental problems in computing.
How to Optimize Language Model Size for Deployment
The rise of language models, and more specifically large language models (LLMs), has been of such a magnitude that it has permeated every aspect of modern AI applications — from chatbots and search engines to enterprise automation and coding assistants.
Dealing with Missing Data Strategically: Advanced Imputation Techniques in Pandas and Scikit-learn
Missing values appear more often than not in many real-world datasets.
Loss Functions Explained: Understand the Maths in Just 2 Minutes Each
I must say, with the ongoing hype around machine learning, a lot of people jump straight to the application side without really understanding how things work behind the scenes.
10 MLOps Tools for Machine Learning Practitioners to Know
Machine learning is not just about building models.
NumPy Ninjutsu: Mastering Array Operations for High-Performance Machine Learning
Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic operations upon data stored as large vectors, matrices, or even tensors — matrix counterparts with three or more dimensions.
10 Python One-Liners That Will Simplify Feature Engineering
Feature engineering is a key process in most data analysis workflows, especially when constructing machine learning models.