(Deprecated) Scikit-learn integration package for Apache Spark

Purely functional genetic algorithms for multi-objective optimisation

Fast Concurrent / Parallel Sorting in Go

Hyperparameter optimization in Julia.

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 Python Toolkit for Managing Large Number of Experiments

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

Online Hackathons/Competitions

Machine Learning Project using Kaggle dataset

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.

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

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

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

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

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

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

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

Design and Implementation of Pac-Man Strategies with Embedded Markov Decision Process in a Dynamic, Non-Deterministic, Fully Observable Environment

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

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.

Automated parameter tuning with deep reinforcement learning

Churn Modelling Using ANN with Parameter tuning for best accuracy

My submission for the DeepTraffic Competition by MIT 6.S094: Deep Learning for Self-Driving Cars

We compared the predictive accuracy and sparsity of support vector machines and relevance vector machines for a range of synthetic data sets differing in signal-to-noise ratio and other measures of difficulty.