Learning to create Machine Learning Algorithms
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Updated
Jun 15, 2021 - Python
Learning to create Machine Learning Algorithms
A TensorFlow-inspired neural network library built from scratch in C# 7.3 for .NET Standard 2.0, with GPU support through cuDNN
A MATLAB toolbox for classifier: Version 1.0.7
Final assignment of a Machine Learning with python Course on Coursera it's purpose is to check and choose the best classification model that predicts if the user can have a loan or not.
This project is to test classification algorithms wrote from scratch in python using only numpy. Algorithms wrote in this project: KNN, Logistic Regression and Naive Bayes classifier.
API-First approach to make Machine Learning solution usable
The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.
Slides and Python code examples for undergraduate machine learning
Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.
This repository contains the necessary scripts to derive off-target models through (1) A neural network framework based on Keras and Tensorflow (2)An autmomated machine learning framework based on AutoGluon
Multi-class classification model for predicting the types of crimes in Toronto
Machine Learning Projects Repository
Git repository for IBM Professional Certification on Data Science
The task is to classify credit card transactions as fraudulent or legitimate.
The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.
An R package for Private Evaporative Cooling feature selection and classification with Relief-F and Random Forests
Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests for classification of Malaria Cells
[Coursera] Deep Learning Specialization on Coursera
Implementation of Google Quick Draw doodle recognition game in PyTorch and comparing other classifiers and features.
PyTorch implementation of Metric-Guided Prototype Learning for hierarchical classification.
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