A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Python package for stacking (machine learning technique)

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Dataflow Programming for Machine Learning in R

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

Building Decision Trees From Scratch In Python

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

I will update this repository to learn Machine learning with python with statistics content and materials

A repository of resources for understanding the concepts of machine learning/deep learning. 

Web application for engineering students to predict appropriate job roles using Machine learning and other guidance material like job descriptions, links to courses, etc.

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Ensemble Learning for Apache Spark 🌲

Entire Machine Learning Hand Written Notes

An analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks

This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.

TextSentimentClassification, using tensorflow.

No description provided.

Sklearn implement of multiple ensemble learning methods, including bagging, adaboost, iterative bagging and multiboosting

Paper: Towards Open-Set Face Recognition using Hashing Functions (IJCB'17)

Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests for classification of Malaria Cells

简单易用的经典机器学习框架

The purpose of this project is to try to predict the occurrence of injuries based on player's in-game statistics.

OCaml Random Forests

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

Implementation of Google Quick Draw doodle recognition game in PyTorch and comparing other classifiers and features.

Used ensemble methods such as boosting, voting, Bagging

It is from Kaggle Competitions where the training dataset is very small and the testing dataset is very large and we have to avoid or reduce overfiting by looking for best possible ways to overcome the most popular problem faced in field of predictive analytics.

Build a predictive machine learning model that could categorize users as either, revenue generating, and non-revenue generating based on their behavior while navigating a website. In order to predict the purchasing intention of the visitor, aggregated page view data kept track during the visit along with some session is used and user information as input to machine learning algorithms. Oversampling/Undersampling and feature selection techniques are applied to improve the success rates and scalability of the models.

FastML Framework is a python library that allows to build effective Machine Learning solutions using luigi pipelines.

Analysed syntax and Semantics of Corpus of Text Documents Retrieved from Web Scraping of News articles from Inshorts and followed the Standard NLP Workflow of the CRISP-DM model.