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Name: 7Sublime

Type: User

7Sublime's Projects

30-days-of-javascript

30 days of JavaScript programming challenge is a step-by-step guide to learn JavaScript programming language in 30 days. This challenge may take more than 100 days, please just follow your own pace.

abigsurvey

A collection of 700+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML)

ajax-movie-recommendation-system-with-sentiment-analysis

Content-Based Recommender System recommends movies similar to the movie user likes and analyses the sentiments on the reviews given by the user for that movie.

bangla-text-classification

Bangla Text Classification using Transformers

bulletproof-react

🛡️ ⚛️ A simple, scalable, and powerful architecture for building production ready React applications.

classification-and-vector-spaces-in-nlp

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. During this project I design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text Classification and Vector Spaces in NLP This is the first course of the Natural Language Processing Specialization. 1: Logistic Regression for Sentiment Analysis of Tweets Use a simple method to classify positive or negative sentiment in tweets 2: Naïve Bayes for Sentiment Analysis of Tweets Use a more advanced model for sentiment analysis 3: Vector Space Models Use vector space models to discover relationships between words and use principal component analysis (PCA) to reduce the dimensionality of the vector space and visualize those relationships 4: Word Embeddings and Locality Sensitive Hashing for Machine Translation Write a simple English-to-French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbors search