Automating Database Updates with GitHub Actions
In many applications, keeping the database up-to-date with fresh data from an API is essential. Manually updating data can be time-consuming and error-prone....
Technical Articles
2025
Building a Chatbot for Mood-Based Trending Movie Recommendation
In my previous post, I showed how to build a RAG (Retrieval-Augmented Generation) mood-based trending movie recommendation app. In this post, we dive into ho...
Building a RAG Mood-Based Trending Movie Recommendation App
In my previous post, I built a mood-based movie recommendation app that used LLM (Large Language Model) to analyze metadata from approximately 50 trending mo...
Handling Validation and Hallucination Issues in LLM for a Mood-Based Movie Recommendation App
Large Language Models (LLMs) are powerful tools for natural language understanding, but they can introduce issues such as validation errors and hallucination...
AI-Agent-Assisted Mood-Based Movie Recommendation
CineMood started as a machine-learning-based mood-based movie recommendation system that analyzed user input and recommended trending movies from The Movie D...
From API to App: Creating a Mood-Based Trending Movie Recommender with Python, Hugging Face, and XGBoost
Ever wondered how to get personalized movie recommendations based on your mood? In this project, I built CineMood (a Mood-Based Trending Movie Recommendation...
Building a Machine Learning Model to Estimate Insurance Premiums
Insurance pricing is a complex process where companies determine premiums based on various risk factors such as age, health, income, claims history, and life...
Step-by-Step Guide to Building a Smart Crop Selection App Using Streamlit
Agriculture plays a crucial role in feeding the world, and selecting the right crop for a given soil type can significantly improve productivity. With Machin...
Smart Crop Selection: Using Machine Learning to Optimize Farming Decisions
In this post, we will work on a project that leverages machine learning to assist farmers in making data-driven decisions about which crop to plant based on ...
Understanding Agentic RAG: A Deep Dive into Intelligent Retrieval and Reasoning Systems
In today’s enterprise AI landscape, accuracy and explainability are non-negotiable. Retrieval-Augmented Generation (RAG) systems have become essential in cus...
Understanding Retrieval-Augmented Generation (RAG): Making LLMs Trustworthy
In an era where large language models (LLMs) are being deployed across industries—from customer service and healthcare to legal and financial services—trust,...
Understanding Prompt Engineering: From Zero-Shot Prompts to Scalable Systems
Prompt engineering has become a critical skill for maximizing the impact of large language models (LLMs) like GPT-4, Claude, and Gemini. It offers a high-lev...
Understanding LLM Serving: How to Run Language Models Fast, Cheap, and Effectively
Large Language Models (LLMs) have redefined what’s possible across industries—from customer support and code generation to internal search and document autom...
Adapting a Pre-trained LLM to a Specific Task, Domain, or Behavior
Pre-trained large language models (LLMs) like GPT-4, Claude, and LLaMA are transforming industries by enabling automation, accelerating content creation, enh...
Understanding LLM Pre-training: Teaching Machines to Think
Large Language Models (LLMs) like GPT-4 and ChatGPT are reshaping how businesses operate — streamlining content creation, automating knowledge work, improvin...
Understanding Transformer Architecture: The Brains Behind Modern AI
Transformers have fundamentally reshaped the AI landscape — powering models like ChatGPT and driving major innovations across Google Search, recommendation e...
A/B Testing with Highly-Skewed Data Using the Mann–Whitney U Test
When your data is highly skewed, traditional A/B tests like the t-test or z-test (see my previous posts P1 and P2) can give misleading results. These tests a...
A/B Testing using T-Test: Making Smart Decisions with Limited Data
In a previous post, we explored how large samples allows us to perform A/B testing based on the z-test and normal distribution. But what happens when your sa...
The Ultimate Guide to A/B Testing: Workflow, Theory, and Python Example
A/B testing is a cornerstone of data-driven decision-making in product development, marketing, and user experience optimization. Whether you’re launching a n...
Metrics for Classification Problems Explained: Accuracy, Precision, Recall, and F1-Score
In the world of machine learning, evaluating a model’s performance is as crucial as building the model itself. Imagine developing a spam email filter or a di...
The Essential Steps of a Successful Data Science Project
In the era of data-driven decision-making, embarking on a data science project requires a structured approach to extract meaningful insights and build impact...