The aim of this repo is to contribute to the diagnosis of epilepsy by taking advantage of the engineering. So, for diagnosing of epileptic seizures from EEG signals are transformed discrete wavelet and auto regressive models. After these transformations, extract data is applied input for Back-propagation, k-Nearest Neighbor (k-NN), Support Vecto…

Advanced topics in ML

Non-contextual : Word2Vec, FastText Contextual : BERT, RoBERTa, ELECTRA, CamemBERT, Distil-BERT, XLM-RoBERTa Analyzed embedding models, used the best one to build a Flask web app for Hindi NER and data collection from user feedback, deployed on AWS.

This repository contains codes, datasets, results, and reports of a machine learning project on air quality prediction.

Collection of some classical Machine learning Algorithms.

A comparative analysis of various machine learning models for time series forecasting, including traditional methods and LLMs.

An NLP research project utilizing the "cardiffnlp/twitter-roberta-base-sentiment-latest" pre-trained transformer for tweet tokenization. The project includes an attention-based biLSTM model that predicts sentiment labels for tweets as negative (-1), neutral (0), or positive (1).

Classical Machine Learning Concepts + Basic WebScraping + Numpy and Pandas libraries

Project to learn linear regression using python, statsmodels and scikit-learn

pylearn_ml191: An implementation of some classical machine learning algorithm

These training sessions in machine learning, conducted by Yandex, are dedicated to classical machine learning. This offers an opportunity to reinforce theoretical knowledge through practice on training tasks.

A case study to identify the major factors which indicate that a customer is about to leave a mobile network. Subsequently, a model is built to predict churn customers in the future.

Repository containing the backend code used for writing a research paper on the prediction of AQI in Indian Cities

Handwritten Digit Classification on MNIST Dataset, Utilising Only Traditional Machine Learning Techniques and a Custom Feature Extractor, achieving highest accuracy of 98.08% with the same.

An attempt at identifying the features which most affect house prices and create a model which can predict house prices.

Ordinary Least Squares, Ridge Regression, Expectation Maximization, Full Bayesian Inference, Bayes Classifiers, kNN, and MLP core algorithms from scratch. Some auxiliary functions are also used.

It will be shown how to train a quantum machine learning model to overcome a classification problem.