Sentiment Analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis on tweets can be extremely useful. By analyzing the tweets, we can find the sentiment of people on certain affair, understand people's opinion. Also it can help us make right strategies/reactions. In this post, our main goal is to build a model to identify tweets with racist or sexist sentiment.

Online shopping now makes our life much easier than it used to be. Without the need of going out and visting a shopping mall or a grocery store, we can buy anything we want through e-shopping. But online shopping comes with its own caveats. One of the biggest challenges, and I guess almost every would face, is verifying the authenticity of a product. Is it as good as advertised on the e-commerce site? Will the product has a good quality? Are the reviews given by other customers really true or are they false advertising, or are they fake or bots' reviews? These are important questions customers need to ask before splurging their money.

In this post, we'll explore how to perform transfer learning using Pytorch.
We will use a subset of Food-11k that contains 11 different kinds of food categories. We will go over the dataset preparation, data augmentation and then steps to build the classifier. We use transfer learning to use the low level image features like edges, textures etc. learnt by a pretrained model, ResNet50, and then train our classifier to learn the higher level details in our dataset images. ResNet50 has already been trained on ImageNet with millions of images.

In this post, we will try to use pre-trained models to do image classification. We will use two popular deep learning frameworks, PyTorch and Keras. Let's find out the workflow of using pre-trained models in these two frameworks.

Time Series Analysis (TSA) is an important part in the field of data science. TSA uses methods for analyzing time series data in order to identify useful patterns and extract meaningful statistics of the data. There are two major goals of TSA: 1) identifing patterns or features represented by the data; and 2) forecasting (using a model to predict future values based on previous data). In this article, we will do a complete machine learning pipeline on analysis time series data. We will use both ARIMA model and Prophet model to predict superstore sales data. These two models are very important in analyzing time series data.