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Machine Learning

Machine Learning guides and implementations in Python, featuring customer segmentation clustering, classification pipelines, model evaluation metrics, and predictive analytics.

Building a Multimodal RAG Pipeline in Python with Qdrant and CLIP Embeddings

Standard Retrieval-Augmented Generation (RAG) pipelines are built exclusively for textual data. They parse markdown files, split paragraphs into token chunks,…

Tool Calling in AI Agents: How LLMs Execute Real-World Actions in 2026

Introduction: Why Tool Calling Is the Backbone of Modern AI Agents Large Language Models (LLMs) are incredibly powerful at understanding…

Creating Your Own PDF Chatbot : LLM

Are you interested in creating your own PDF chatbot but want to have full control over every aspect of the…

A Comprehensive Guide to Hugging Face Transformers

In the vast field of natural language processing (NLP), Hugging Face Transformers has emerged as a go-to library for leveraging…

Predicting House Prices using Machine Learning: A Step-by-Step Guide

As the real estate market continues to grow, there is an increasing demand for accurate predictions of house prices. With…

Confusion matrix – Example, Scenario and Code

A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier")…

Evaluating the model performance [Deep Understanding] – Machine Learning

Evaluating the performance of a machine learning model is an important step in the model development process, as it allows…

Machine Learning – Random Forest

Random forests are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are…

Machine Learning – Model Evaluation

Model evaluation is the process of assessing the performance of a model on a dataset. This is typically done by…

Machine Learning – Model Building

Machine learning is a type of artificial intelligence that allows computer programs to learn from data and improve their performance…