Feature Engineering Tutorial Series 4: Linear Model Assumptions

Linear models make the following assumptions over the independent variables X, used to predict Y: There is a linear relationship between X and the outcome Y The independent variables X are normally distributed There is no or little co-linearity among the independent variables Homoscedasticity (homogeneity of variance) Examples of linear Read more…

Word Embedding and NLP with TF2.0 and Keras on Twitter Sentiment Data

Word Embedding and Sentiment Analysis What is Word Embedding? Natural Language Processing(NLP) refers to computer systems designed to understand human language. Human language, like English or Hindi consists of words and sentences, and NLP attempts to extract information from these sentences. Machine learning and deep learning algorithms only take numeric Read more…

Amazon and IMDB Review Sentiment Classification using SpaCy

Sentiment Classification using SpaCy What is NLP? Natural Language Processing (NLP) is the field of Artificial Intelligence concerned with the processing and understanding of human language. Since its inception during the 1950s, machine understanding of language has played a pivotal role in translation, topic modeling, document indexing, information retrieval, and Read more…

2D CNN in TensorFlow 2.0 on CIFAR-10 – Object Recognition in Images

What is CNN This Notebook demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. Unlike traditional multilayer perceptron architectures, it uses two operations Read more…

Text Generation using Tensorflow, Keras and LSTM

Automatic Text Generation Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the Read more…

SpaCy – Introduction for NLP | Combining NLP Models and Custom rules

Combining NLP Models and Creation of Custom rules using SpaCy Objective: In this article, we are going to create some custom rules for our requirements and will add that to our pipeline like explanding named entities and identifying person’s organization name from a given text. For example: For example, the Read more…