AI Engineering
Practical AI projects, experiments, tutorials, and applied research.
A Beginner-Friendly Guide to Explainable AI (XAI)
Curious how AI models make decisions—or what goes on inside their “black box”?
In my latest blog, I break down explainable AI with hands-on Python examples, using real tools like SHAP, LIME, ELI5, and DALEX. Whether you’re…
How to Detect Hidden Bias in Your ML Model — A Step-by-Step Tutorial
Ever wondered how AI decides who gets hired or approved for a loan?
Spoiler: it sometimes inherits our biases.
In my latest blog, I uncover how bias sneaks into machine learning—and how we can fix it to make…
A Beginner’s Guide to Time Series Modeling
Ever wondered how AI predicts tomorrow’s weather, stock prices, or even your heart rate? In my latest blog, I break down how machines learn from the past to predict the future — exploring trends, seasonality, and…
Transfer Learning for Multimodal Models
Ever searched for a product just by uploading a picture — and instantly found what you wanted?
That’s multimodal AI at work. These models combine images with text metadata like titles, brands, and descriptions to make search…
Transfer Learning for Vision
Ever wondered how AI can detect cancer from scans or spot heart disease in an X-ray — without being trained on millions of medical images?
Here’s the secret: Transfer Learning.
Instead of building models from scratch, researchers take…
Transfer Learning for NLP
Ever wondered how AI learns new languages so quickly — or how ChatGPT seems to instantly “get” what you’re saying?
That’s the power of transfer learning in Natural Language Processing (NLP) — where AI learns language the…
AI Engineering brings together practical AI projects, experiments, tutorials, and applied research notes on building real-world AI systems. The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes.