Best 1-Page Resource

Best short notes, 1-page resources for interviews. Save them, Keep it with you. These will help you before going to any interviews for sure.

RAG Background

RAG

2024

RAG here is explained in very simpler way. Retrieval-Augmented Generation (RAG) is a powerful AI method that enhances LLM responses.

ML Background

ML

DEC 2024

These are some machine learning model which you should know if you are an aspiring data scientists.

DEEPSEEK R1 Background

DEEPSEEK R1

JAN 2025

They used 3 most important techniques which was explained in their research paper.

Vector Search Background

Vector Search

JAN 2025

They used 3 most important techniques which was explained in their research paper.

LLM Explained Background

LLM Explained

FEB 2025

A Large Language Model (LLM) is a sophisticated mathematical function that predicts the next word in a given piece of text. It forms the foundation of text-based AI models, such as those developed by OpenAI, DeepSeek, and other chatbot systems.

Data Cleaning Background

Data Cleaning

FEB 2025

All data cleaning process methods explaiened here in one short note. .

Python String Methods Background

Python String Methods

FEB 2025

All python string methonds here in one short note.

RAG Pipeline Background

RAG Pipeline

FEB 2025

RAG Pipe Explained in deatiled in just one page.

 AI Agents Background

AI Agents

FEB 2025

AI Agents explained in details in just one page.

 Confusion Matrix Background

Confusion Matrix

FEB 2025

Performance measurement for machine learning classification.

 Linear Regression Background

Linear Regression

FEB 2025

Use fo predicting the value of a continuous dependent variable based on one or more independent variables.

 Types Of Machine Learning Background

Types Of Machine Learning

FEB 2025

Types of machine learning which every Data Scientists should know.

 Dustance Matrics In ML Background

Dustance Matrics In ML

FEB 2025

Types of distance matrics present in machine learning.

 Data Cleaning Steps Background

Data Cleaning Steps

FEB 2025

8 Most important steps invloved in data cleaning.