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100-days-of-nlp

av_loan_preds

Analytics Vidhya Loan Prediction problem, just stretching out my fingers..

awesome-seml

A curated list of articles that cover the software engineering best practices for building machine learning applications.

basic-guide-to-nlp

This repository contains a Jupyter notebook with sample codes from basic to major NLP processes required for dealing with text.

bert-imdb_reviews

Playing around with the bert tokenizer and TF2 to classify IMDB Sentiments, model achieves state-of-the-art performance.

cn

comment-quality-analysis-cnn-lstm

NLP- Deep Learning- AI Model built to analyze a comment given by a supervisor to their employee in real-time to check how "Effective" that feedback is.

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

dm

dp100

Labs for Course DP-100: Designing and Implementing Data Science Solutions on Microsoft Azure

enron_intent_detection

Intent detection on Enron email set. We define "intent" here to correspond primarily to the categories "request" and "propose". In some cases, we also apply the positive label to some sentences from the "commit" category if they contain datetime, which makes them useful. Detecting the presence of intent in email is useful in many applications, e.g., machine mediation between human and email. The dataset contains parsed sentences from the email along with their intent (either 'yes' or 'no'). You need to build a learning model which detects whether a given sentence has intent or not. Its a 2-class classification problem.

flairnlp_experiment_1

Experimenting with FlairNLP Text Classification for the first time for a kaggle competition to detct real disaster or not tweets.

fuzzystringmatchingofstocknameswith-r

Stocks listed on the NYSE and S&P500 index are compared. The names existing on both lists are compared using a fuzzy approach and a truth value is tried to be determined, in order to generate a collinearity between them.

hr_analytics_pratise_problem

Your client is a large MNC and they have 9 broad verticals across the organisation. One of the problem your client is facing is around identifying the right people for promotion (only for manager position and below) and prepare them in time. Currently the process, they are following is: They first identify a set of employees based on recommendations/ past performance Selected employees go through the separate training and evaluation program for each vertical. These programs are based on the required skill of each vertical At the end of the program, based on various factors such as training performance, KPI completion (only employees with KPIs completed greater than 60% are considered) etc., employee gets promotion For above mentioned process, the final promotions are only announced after the evaluation and this leads to delay in transition to their new roles. Hence, company needs your help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle. They have provided multiple attributes around Employee's past and current performance along with demographics. Now, The task is to predict whether a potential promotee at checkpoint in the test set will be promoted or not after the evaluation process.

mslearn-dp100

Lab files for Azure Machine Learning exercises

nlp-notebooks

A collection of notebooks for Natural Language Processing from NLP Town

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

stockpricepredictionusing_xgboost

College Project

transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.