A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

(Deprecated) Scikit-learn integration package for Apache Spark

Workflow engine for exploration of simulation models using high throughput computing

Hyperparameter optimization in Julia.

Purely functional genetic algorithms for multi-objective optimisation

Fast Concurrent / Parallel Sorting in Go

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

A hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-/deep-learning models.

A Python Toolkit for Managing Large Number of Experiments

Trying PostgreSQL parameter tuning using machine learning.

Online Hackathons/Competitions

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

Machine Learning Project using Kaggle dataset

a case study on deep learning where tuning simple SVM is much faster and better than CNN

The project has text vectorization, handling big data with merging and cleaning the text and getting the required columns while boosting the performance by feature extraction and parameter tuning for NN, compares the Performances through applied different models treating the problem as classification and regression both.

Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However, to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level behaviours.

Bayesian Optimisation for Parameter Tuning of the XOR Neural Network

Algorithm Configuration Visualizations for irace!

An sklearn type class for testing multiple models on data with sequential hyper-parameter tuning for the models

It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achieve the Accuracy. I got a very Satisfiable Accuracy from the Model in both the Training and Testing Sets.

Tuning of parameters of ML algorithms to optimise precision/f-score for fault detection in softwares

Elegant Mathematica-style model manipulation, fitting and exploration in MATLAB.

Classifying patients based on six features

Codes and templates for ML algorithms created, modified and optimized in Python and R.

Resample, parameter tuning, meta-learning, clustering, and mining algorithms for the purpose of data mining and machine learning.

🌿 Introduce surrogate based-optimization beside evolutionary algorithms that can significantly influence on the effort being spent multi-objective parameter tuning

This classifiers the gender of the person speaking in the singular audio file using Artificial Neural Networks

Automated parameter tuning with deep reinforcement learning

MPS-APO is a rapid and automatic parameter optimizer for multiple-point geostatistics