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awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

build-your-own-x

Master programming by recreating your favorite technologies from scratch.

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

design-patterns-using-java

SCS2203 - Software Engineering

dev-v

Cuticare is a med mobile application that's build on pretrained model(uses transfer learning approach), Resnet50 has been trained on the ImageNet dataset to identify common skin diseases such as Eczema,acne, Psoriasis,Melanoma etc..

dev-v-1

Skin Disease Detection system built on an application comprising of a front-end and a back-end that uses Machine learning, Deep learning, and image processing techniques to identify skin diseases.

dsa-

java

feature-selection-techniques

firstflaskapp

ggit-demo

gnnpapers

Must-read papers on graph neural networks (GNN)

iit-playframework-session

Play framework as a backend session (03 week session series) that I have conducted for the Informatics Institute of Technology L5 students

loancalc

Simple java program to calculate mortgage loan

ml-cookies

cooking basic models and understandings

ml-datamining-cw

Machine learning-based wine quality prediction model R Acknowledgement In this assignment, we consider a set of observations on a number of white wine varieties involving their chemical properties and ranking by tasters. Wine industry shows a recent growth spurt as social drinking is on the rise. The price of wine depends on a rather abstract concept of wine appreciation by wine tasters. Pricing of wine depends on such a volatile factor to some extent. Another key factor in wine certification and quality assessment is physicochemical tests which are laboratory-based and takes into account factors like acidity, pH level, presence of sugar and other chemical properties. For the wine market, it would be of interest if human quality of testing can be related to the chemical properties of wine so that certification and quality assessment and assurance process is more controlled. One dataset (whitewine_v2.xls) is available of which is on white wine and has 4710 varieties. All wines are produced in a particular area of Portugal. Data are collected on 12 different properties of the wines, one of which is Quality (i.e. the last column), based on sensory data, and the rest are on chemical properties of the wines including density, acidity, alcohol content etc. All chemical properties of wines are continuous variables. Quality is an ordinal variable with possible ranking from 1 (worst) to 10 (best). Each variety of wine is tasted by three independent tasters and the final rank assigned is the median rank given by the tasters.

ml-design-patterns

Source code accompanying O'Reilly book: Machine Learning Design Patterns

ml-for-beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

models

Models and examples built with TensorFlow

networkintrusiondetection

This repository is for exploring various ML and DL techniques for network intrusion detection

neural-odes-segmentation

Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

pytorch-mask-rcnn

learning

sales-prediction-analysis-

Carrying on exploratory analysis of the data, verifying the data schema, handling missing values and evaluating sales performance and profitability across regions, and visualizing patterns and trends in the data.\

stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

textmate

TextMate is a solution that allows users to extract meaning from users reviews

the-algorithm

Source code for Twitter's Recommendation Algorithm

unlearning_for_mri_harmonisation

Code for implementation of Unlearning Scanner Bias for MRI Harmonisation

zero-to-hero-with-github

zero to hero with GitHub