This is a coursework on foundations of computing module
Through this coursework i worked on methods and classes in python. This was done by analysing some cryptocurrency data and creating a linear regression to predict cryptocurrency prices in the future.
i hope you enjoy it
Summer: compartmental disease modelling in Python Summer is a Python-based framework for the creation and execution of compartmental (or "state-based"
moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.
pm-prophet Pymc3-based universal time series prediction and decomposition library (inspired by Facebook Prophet). However, while Faceook prophet is a
The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv
End of maintenance for GPyOpt Dear GPyOpt community! We would like to acknowledge the obvious. The core team of GPyOpt has moved on, and over the past
**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** S
Model Search Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers sp
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query
STUMPY STUMPY is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of tim
duality Data science, Data manipulation and Machine learning package. Use permitted according to the terms of use and conditions set by the attached l
To design and implement the Identification of Iris Flower species using machine learning using Python and the tool Scikit-Learn.
Advanced-Machine-Learning This repository contains the code for the project Real
metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met
Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.
DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong
Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validat
This repo implements a topological SLAM system. Deep Visual Odometry (DF-VO) and Visual Place Recognition are combined to form the topological SLAM system.
FFT-accelerated Interpolation-based t-SNE (FIt-SNE) Introduction t-Stochastic Neighborhood Embedding (t-SNE) is a highly successful method for dimensi
This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment to test the algorithm
Dragonfly is an open source python library for scalable Bayesian optimisation. Bayesian optimisation is used for optimising black-box functions whose
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