Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Implementations of essential machine learning algorithms from scratch
Analyse von Datensätzen mit verschiedenen ML-Algorithmen
The Fast Gradient Sign Method (FGSM) combines a white box approach with a misclassification goal. It tricks a neural network model into making wrong predictions. We use this technique to anonymize images.
Machine Learning library using what I learned from CS4780, using NumPy only. It supports Bayesian inference, kernelization, ensembles, deep learning, convolutional NN, and Transformers.
All my learnings from "Machine Learning with Python" course offered by "IBM" on Coursera are reflected here.
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
DU - DA Module 20 challenge
"This repository contains implementations of Boosting method, aimed at improving predictive performance by combining multiple models. by using titanic database."
This repository contains a Python implementation of a Multiple Linear Regression model to predict a company's profit based on various expenditures and the company's state.
Provide exploratory data analysis of the water level dataset from the Three Gorges dam in China as well as develop a machine learning model to forecast upstream water levels.
S.O.L.I.D. Principles for Machine Learning project.
"This repository contains implementations of Boosting method, popular techniques in Model Ensembles, aimed at improving predictive performance by combining multiple models. by using titanic database."
Bachelor Thesis, Lennart Keidel - Machine Learning in Games of Imperfect Information
A concept on how Machine Learning (ML) can be integrated on Web apps
Machine learning multi classification model
Return over investment machine learning regression model
This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.
This project employs machine learning for early autism detection. Utilizing Python and SVM, it offers two models: one trained on a verified dataset for classification, and another for real-time prediction from user input, enhanced with visualizations for insightful analysis.
Perform exploratory data analysis and develop machine learning models to a telecom customer churning dataset
MLModelScope is an open source, extensible, and customizable platform to facilitate evaluation and measurement of ML models within AI pipelines.