Twitter Sentiment Analysis
In this article, we will discuss how to implement sentiment analysis on a raw tweets dataset in python, find the polarity scores to classify the sentiments, and train a Long short-term memory (LSTM) and Convolutional Neural Network to predict the sentiment polarity and compare the results. Reference
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Introduction to VECTOR EMBEDDING
VECTOR EMBEDDING : it’s a technique for learning numerical representations for token (word) that approximate their lexical meaning.these representations are learned by observed words in their context of occurrence in large volumes of data.
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The State of Multilingual AI
Models that allow interaction via natural language have become ubiquitious. Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. At the same time, a wave of NLP startups has started to put this technology to practical use. While such language technology may be hugely impactful, recent models have mostly focused on English and a handful of other languages with large amounts of resources. […]
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Predicting the outcome of the World Cup in 150 lines of Python
It seems like Machine learning and Deep Learning are everywhere these days. AI can do anything! From writing essays to cheat tests to generating to having an infinite image generating machine running on your local machine, the possibilities are endless. So it should come as no surprise that you can use deep learning to predict the outcome of major sports tournaments.
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K-Means Clustering: A Centroid-based Algorithm
K — means clustering is a centroid-based unsupervised machine learning algorithm. Unsupervised learning uses the machine learning algorithm to analyze unlabelled data and find hidden patterns without human intervention. It’s clear from the name itself that K-means is a cluster-based algorithm. Clustering is a technique where we can group together a set
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