Dropout in Deep Learning

Fraud detection over twitter feed data

A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis

Python version of Andrew Ng's Machine Learning Course.

A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.

Implementation of Decision Tree and Random Forest algorithms, with various hyperparameters, developed from scratch and using scikit-learn for comparison and analysis.

Adding noise as regularization method to reduce overffiting in neural networks

Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI

Evaluating classifier using Python focus on evaluation metrics and hyperparameter turning

This project builds an Income Prediction model using Decision Tree Regression. It includes full data preprocessing, categorical encoding, log transformation, and hyperparameter tuning with GridSearchCV. The project demonstrates an end-to-end ML workflow and highlights model performance on a noisy synthetic dataset. Topics

Make use of PyTorch's custom modules to define a network architecture and train a model. Investigate how to improve a model's performance and deploy your model for wider use.

A repository documenting the work for the Special Course with Professor Søren Hauberg from DTU Cognitive Systems.

Supervised Learning - Regression Algorithm

Brief study on Underfitting and Overfitting in Machine Learning

This repository provides a series of interactive Jupyter Notebook exercises designed to teach fundamental deep learning concepts through hands-on implementation and experimentation.

Overfitting and Underfitting in Machine Learning

IMP KEYS OF ML MODEL

Overfitting is often caused by using a model with too many parameters or if the model is too powerful for the given dataset. On the other hand, underfitting is often caused by the model with too few parameters or by using a model that is not powerful enough for the given dataset. In this we are discussing about that.

In this repository you will learn how to handle overfitting with the help of Lasso and Ridge Regression regularizations, also working mechanism of those while using useful charts.

This project helps exercising my machine learning modelling skills and evaluation techniques on image-based models.