azizamirsaidova

abstractive_text_summarization

Abstractive text summarization using CNN/DailyMail Dataset training on RNN/LSTM & T5.

aimodels

Experimenting with AI models in a simple way.

answer_classification

Classifying answer types and categories to optimize answer generation process for QA systems.

aws_disaster_response_hackathon

Tornado Prediction for Amazon Disaster Response Hackathon

azizamirsaidova

My personal repo

azizamirsaidova.github.io

My portfolio website.

fake-news-detection

A Linguistic Evaluation of Machine-Generated “Real” and “Fake” News

nlp_language_models

Building LSTM and RNN models for WikiText-2 dataset.

sign-language-interpreter

Sign Language Interpreter using Leap Motion Sensors dataset and ML Classification techniques.

SSS

Database Code

azizamirsaidova/bayesian_tomatoes

This lab will cover: Making and interpreting predictions from a Bayesian perspective Using the Naive Bayes algorithm to predict whether a movie review is positive or negative Using cross validation to optimize models

azizamirsaidova/Game-of-Drones

azizamirsaidova/DataAndBases

azizamirsaidova/datadive

A collection of data science projects.

azizamirsaidova/esg-nlp

Analysing ESG report using Natural Language Processing

azizamirsaidova/HackDay

Hack Day for Machine Learning Code-First Basics from Univ.AI

azizamirsaidova/heart

Harnessing data to help the heart. ML-2: Trees, Model Interrogation and Bayesian Workflow

azizamirsaidova/hmw3

azizamirsaidova/homework_2

azizamirsaidova/lab4_training

azizamirsaidova/LearningAModel

Part 1b of Machine Learning code-First Basics

azizamirsaidova/literature_search

Automated Literature Analysis

azizamirsaidova/pre-post-covid-analysis

azizamirsaidova/prettymaps

A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.

azizamirsaidova/Regression

Part 1a of Machine Learning Code-First Basics

azizamirsaidova/Restaurant-Recommendation-System

A recommendation system for restaurants using collaborative filtering (CF). We will be using the Yelp Dataset for this.

azizamirsaidova/rossman_forecasting_sales

This project looks into the practicalities of Trees, MLPs and Entity Embedding. The homework is divided into four main parts: Data-preprocessing Developing different models and evaulating the models - without Entity Embeddings Pass on the entity embeddings from Neural Network model to other models and evaluate the models Compare the models

azizamirsaidova/santander_customer_satisfaction

Perform predictive modeling to predict the probability of each customer being unsatisfied.

azizamirsaidova/univ.ai.ml

Univ.Ai's Data Science and Machine Learning program exercises, labs and homework.

azizamirsaidova/ValidationRegularization

Part 2a of Machine Learning Code-First Basics