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catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

coding-interview-university

A complete computer science study plan to become a software engineer.

concise-concepts

This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity scoring.

covid-19-markovchain-probability-transition-matrix

This is a Mathematica notebook that contains genomic sequence of COVID-19 and calculates the n-th order Markov Chain probability transition matrices of it.

dash-clustering

Plotly Dash web app for cluster analysis.

facet

Human-explainable AI.

gpt-vision-for-finance

Simple Streamlit app to chat with image data

interpretable-ml-book

Book about interpretable machine learning

lagrangian_nns

Lagrangian Neural Networks

longformer

Longformer: The Long-Document Transformer

ml-material

pandas-montecarlo

A lightweight Python library for running simple Monte Carlo Simulations on Pandas Series data

portfolio-optimization-using-genetic-algorithm

Portfolio optimization using Genetic algorithm.

portfoliooptimizer

Using genetic algorithm for the financial problem of optimizing investment portfolio

pyndamics

Python Numerical Dynamics Simulator

shap

A game theoretic approach to explain the output of any machine learning model.

spx_prediction_lstm

Using LSTM to predict SPX index

stock-price-prediction-model

Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity, Objectivity, Positive, Negative, Neutral) data is gathered from the news data and further used to predict stock prices. Achieved an accuracy of 94% using XGBoost.

streamlit-fastapi-model-serving

Simple web app example serving a PyTorch model using streamlit and FastAPI

stt-emotion-classifier

This app records your voice and then pass it to a ROBERTA based sentiment classifier. In return you will get a table of your probable emotions!

tutorials

CatBoost tutorials repository