Data Science

This Document has notes about the very first Machine learning Algorithm, the linear regression algorithm.

This Document has notes about NLP and steps involved in Text Data Processing.

This document provides steps in unsupervised learning and explains when to use one.

This Document helps understand how to balance an imbalanced data set for better prediction.

This document covers Ensemble learning, bagging, bootsrapping, boosting, random forest and XGB in detail, along with their pros and cons. Ensemble learning means combination of available ML algorithm.

This document explains decision tree algorithm used in regression and classification problems.

This Document is about K-Nearest Neighbour algorithm, a supervised learning technique, used for regression as well as classification. This notes will help one to navigate the significance of KNN model. Also including how to use and when to use this model in real time scenarios.

What if the target problem is categorical? in that case we must know that the task is classification and Classification comes under supervised learning. This Document explains Logistic regression, which is a classification algorithm.

This Document gives a clear introduction about Machine Learning and this is the transition point from understanding the logics and Math behind the problems and implementing the knowledge to Business questions.

This is a series of decks overwritten with notes about processing image and text data using python libraries.