Logistic Regression and Naive Bayes are two most commonly used statistical classification models in the analytics industry. Logistic Regression, a discriminative model, assumes a parametric form of class distribution Y given data X, P(Y|X), then […]
This blog-post is the subsequent part of my previous article where the fashion MNIST data-set was described. Also, we wrote data loader functions in the blog-post. In this article, we will focus on writing python […]
Table of Contents 1 Importing Libraries 2 User Defined Functions 3 Reading Data 3.1 Checking the event rate 4 Displaying the attributes 5 Checking Data Quality 6 Missing Value Treatment 7 Looking at attributes (EDA) 8 Preparing Data for Modeling 9 Model 1 – XGB […]
As in my previous post “Setting up Deep Learning in Windows : Installing Keras with Tensorflow-GPU”, I ran cifar-10.py, an object recognition task using shallow 3-layered convolution neural network (CNN) on CIFAR-10 image dataset. We achieved […]
Text classification is a problem where we have fixed set of classes/categories and any given text is assigned to one of these categories. In contrast, Text clustering is the task of grouping a set of unlabeled texts in […]
Keystroke dynamics is the study of the typing patterns of people to distinguish them from one another, based on of these patterns. Every user has a certain way of typing that separates him from other […]
The Naive Bayes classifier is a frequently encountered term in the blog posts here; it has been used in the previous articles for building an email spam filter and for performing sentiment analysis on movie reviews. Thus […]
This is a description of mathematical formulation for understanding SVM classification.
tutorial on sentiment analysis on movie reviews using machine learning techniques. It describes famous tf-idf text features for text classification task.
shows python based tutorial on text classification of emails into spam and non-spam categories. It uses bag of word features and machine learning models.