Compendium of free ML reading resources

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

High-performance, Composable framework for Fully On Chain Games and Autonomous Worlds

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization

NLP & Classification exercises and projects done at alx training

Machine Learning

This repository contains a comprehensive guide and implementation of ensemble modeling techniques, specifically focusing on Boosting, Bagging, and Voting. Ensemble methods are powerful techniques in machine learning that combine the predictions from multiple models to improve overall performance and robustness.

MlFlow Project creating pipelines and using Grid-Search Cross Validation to find optimal parameters for Old School Runescape Machine Learning datasets.

This repository contains implementations of various machine learning algorithms from scratch (KNN, MSE Linear Regression, SVM, Neural Networks, Logistic Regression). Each algorithm is implemented in Python and is contained in its own directory.

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

Batch Name: MIP-ML-11 (Machine Learning Intern)

Sign Language Detection & Translation Web App

This repository is about a trained Machine Learning model which predicts Whether the Heart Disease is present or not by considering few factors. This ML model is selected by considering different accuracies of various trained ML models.

Code associated wth the InterpretE research paper

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

Implementation of unconstrained and constrained convex optimization algorithms in Python, focusing on solving data science problems such as semi-supervised learning and Support Vector Machines.

I have implemented support vector machine classifier on the same dataset but using different kernels and have compared their accuracies with each other

Built a deep learning-based model to recommend movies based on user sentiment. Extracted data using Twitter API, preprocessed data using NLTK, and built machine learning models using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classification methods. Deployed the model on Airflow/EC2 and stored results in Amazon S3. Achieved 70%

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.