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sridharkumarkannam's Projects

artificial-intelligence-deep-learning-machine-learning-tutorials icon artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

brownlee icon brownlee

Code snippets from Jason Brownlee's ML and Deep Learning books.

citrine_ml_tut icon citrine_ml_tut

ML tutorials from Citrine (http://www.citrine.io/blog/?offset=1434641580000)

community-code icon community-code

Shared code for data transforms, calling your model or just having fun with deep learning

datasciencer icon datasciencer

a curated list of R tutorials for Data Science, NLP and Machine Learning

h2o-meetups icon h2o-meetups

Presentations from H2O meetups & conferences by the H2O.ai team

h2o-tutorials icon h2o-tutorials

Tutorials and training material for the H2O Machine Learning Platform

h2o_tutorials icon h2o_tutorials

Slides and code examples for H2O tutorials at various events

humanactivityrecognition icon humanactivityrecognition

This project is to build a model that guesses the human activities like Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.

islr-python icon islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

kaggle_titanic icon kaggle_titanic

This notebook uses data from Kaggle competition "Titanic: Machine Learning from Disaster". Firstly, I engineer features to get maximum from the limited data available, and fill missing values of Age variable using linear regression. Then I compare performance of multiple classifiers, such as: logistic regression, SVM, k - nearest neighbours, Naive Bayes, Decision Tree Classifier. I also use ensemble methods such as random forest, bagging, boosting and voting classifier. I use grid search and cross validation to tune up the parameters of classifiers.

lammps icon lammps

Public/backup repository of the LAMMPS MD software package

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