Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
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Updated
Jul 12, 2019 - Jupyter Notebook
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
Python package for stacking (machine learning technique)
I will update this repository to learn Machine learning with python with statistics content and materials
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.
A repository of resources for understanding the concepts of machine learning/deep learning.
Dataflow Programming for Machine Learning in R
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.
Web application for engineering students to predict appropriate job roles using Machine learning and other guidance material like job descriptions, links to courses, etc.
An example repo for how PU Bagging and TSA works.
TextSentimentClassification, using tensorflow.
Predictive Machine Learning projects
Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/
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.
Ensemble Learning for Apache Spark 🌲
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.
Introduction to tree models with Python
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