Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.
Bayesian optimization in JAX
timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s
You can perform Land Cover Classification on Satellite Images using Random Forest and visualize the result using Earthpy package. Make sure to install the required packages and such as
A machine learning project that predicted binding sites of AP1 transcription factor, using ChIP-Seq data and local DNA shape information.
churn_model MLOps Toy Example End to End You might find below links useful Connect VSCode to Git MLFlow Port Heroku App Project Organization ├── LICEN
early-ML Presentation General Machine Learning tutorials A Python step-by-step primer for Machine Learning and Optimization This github repository gat
geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins
Logistic_Regression_Model A logistic regression model for health insurance purchasing prediction This code is using these packages, so please make sur
The kalman filter framework described here is an incredibly powerful tool for any optimization problem, but particularly for visual odometry, sensor fusion localization or SLAM.
House-Price-Prediction-Using-Linear-Regression The dataset I used for this personal project is from Kaggle uploaded by aariyan panchal. Link of Datase
Send Rockets To Mars With AI Send rockets to Mars with artificial intelligence(Genetic algorithm) in python. Tools Python 3 EasyDraw How to Play Insta
Nevergrad - A gradient-free optimization platform nevergrad is a Python 3.6+ library. It can be installed with: pip install nevergrad More installati
stability-selection - A scikit-learn compatible implementation of stability selection stability-selection is a Python implementation of the stability
Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed
Predict profitability of trades based on indicator buy / sell signals Trade profitability analysis for trades based on various indicators signals: MAC
dsnk-distributions package dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calcu
Diabetes Prediction with Logistic Regression Exploratory Data Analysis Data Preprocessing Model & Prediction Model Evaluation Model Validation: Holdou
Common machine learning models' hyperparameter tuning This repo is for a collection of hyper-parameter tuning for "common" machine learning models, in
R-Solver A Python tools for deriving R-Type adaptors for Wave Digital Filters. This code is not quite production-ready. If you are interested in contr
List of Implementations: Currently, the reimplementation of the DeepAR paper(DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
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