-microsoft-professional-program-for-data-science-project

Predicting Mortgage Approvals from Government Data GOAL - To predict whether a mortgage application was accepted (meaning the loan was originated) or denied according to the given dataset, which is adapted from the Federal Financial Institutions Examination Council (FFIEC).

algorithm-trading-and-stock-prediction-using-machine-learning

ALGORITHM TRADING AND STOCK PREDICTION USING MACHINE LEARNING

bearing-fault-diagnosis-cnn

Siamese network for bearing fault diagnosis

books-and-papers

Books and Papers for Artificial Intelligence

boxify_app

boxify is a service with a mobile application that connects customers to restaurants and stores that have surplus unsold food.

copddetectionusingacoustics

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

courseworkshop

deep-reinforcement-learning-hands-on-second-edition

Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt

emmanuellwele

flutter

Flutter makes it easy and fast to build beautiful apps for mobile and beyond.

flutter-complete-guide-course-resources

Course resources (code snapshots & slides) for our complete Flutter & Dart course (https://acad.link/flutter).

flutter-cookbook

Flutter Cookbook, published by Packt

flutterexampleapps

[Example APPS] Basic Flutter apps, for flutter devs.

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

interview-coding-challenge-data-science

Interview Coding Challenge Data Science Step 1 of the Data Scientist Interview process. Follow the instructions below to complete this portion of the interview. Please note, although we do not set a time limit for this challenge, we recommend completing it as soon as possible as we evaluate candidates on a first come, first serve basis... If you have any questions, please feel free to email [email protected]. We will do our best to clarify any issues you come across. Prerequisites: A Text Editor - We recommend Visual Studio Code for the ClientSide code, its lightweight, powerful and Free! (https://code.visualstudio.com/) SQL Server Management Studio (https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) R - Software Environment for statitistal computing and graphics. You can download R at the mirrors listed here (https://cran.r-project.org/mirrors.html) Azure - Microsoft's Cloud Computing platform. You can create an account without a credit card by using the Azure Pass available at this link (https://azure.microsoft.com/en-us/offers/azure-pass/) Git - For source control and committing your final solution to a new private repo (https://git-scm.com/downloads) a. If you're not very familiar with git commands, here's a helpful cheatsheet (https://services.github.com/on-demand/downloads/github-git-cheat-sheet.pdf) 'R' Challenge For each numbered section below, write R code and comments to solve the problem or to show your rationale. For sections that ask you to give outputs, provide outputs in separate files and name them with the section number and the word output "Section 1 - Output". Create a private repo and submit your modified R script along with any supporting files. Load in the dataset from the accompanying file "account-defaults.csv" This dataset contains information about loan accounts that either went delinquent or stayed current on payments within the loan's first year. FirstYearDelinquency is the outcome variable, all others are predictors. The objective of modeling with this dataset is to be able to predict the probability that new accounts will become delinquent; it is primarily valuable to understand lower-risk accounts versus higher-risk accounts (and not just to predict 'yes' or 'no' for new accounts). FirstYearDelinquency - indicates whether the loan went delinquent within the first year of the loan's life (values of 1) AgeOldestIdentityRecord - number of months since the first record was reported by a national credit source AgeOldestAccount - number of months since the oldest account was opened AgeNewestAutoAccount - number of months since the most recent auto loan or lease account was opened TotalInquiries - total number of credit inquiries on record AvgAgeAutoAccounts - average number of months since auto loan or lease accounts were opened TotalAutoAccountsNeverDelinquent - total number of auto loan or lease accounts that were never delinquent WorstDelinquency - worst status of days-delinquent on an account in the first 12 months of an account's life; values of '400' indicate '400 or greater' HasInquiryTelecomm - indicates whether one or more telecommunications credit inquires are on record within the last 12 months (values of 1) Perform an exploratory data analysis on the accounts data In your analysis include summary statistics and visualizations of the distributions and relationships. Build one or more predictive model(s) on the accounts data using regression techniques Identify the strongest predictor variables and provide interpretations. Identify and explain issues with the model(s) such as collinearity, etc. Calculate predictions and show model performance on out-of-sample data. Summarize out-of-sample data in tiers from highest-risk to lowest-risk. Split up the dataset by the WorstDelinquency variable. For each subset, run a simple regression of FirstYearDelinquency ~ TotalInquiries. Extract the predictor's coefficient and p-value from each model. Store the in a list where the names of the list correspond to the values of WorstDelinquency. Load in the dataset from the accompanying file "vehicle-depreciation.csv". The dataset contains information about vehicles that our company purchases at auction, sells to customers, repossess from defaulted accounts, and finally re-sell at auction to recover some of our losses. Perform an analysis and/or build a predictive model that provides a method to estimate the depreciation of vehicle worth (from auction purchase to auction sale). Use whatever techniques you want to provide insight into the dataset and walk us through your results - this is your chance to show off your analytical and storytelling skills! CustomerGrade - the credit risk grade of the customer AuctionPurchaseDate - the date that the vehicle was purchased at auction AuctionPurchaseAmount - the dollar amount spent purchasing the vehicle at auction AuctionSaleDate - the date that the vehicle was sold at auction AuctionSaleAmount - the dollar amount received for selling the vehicle at auction VehicleType - the high-level class of the vehicle Year - the year of the vehicle Make - the make of the vehicle Model - the model of the vehicle Trim - the trim of the vehicle BodyType - the body style of the vehicle AuctionPurchaseOdometer - the odometer value of the vehicle at the time of purchase at the auction AutomaticTransmission - indicates (with value of 1) whether the vehicle has an automatic transmission DriveType - the drivetrain type of the vehicle

java-books

Java Books

java9-thecompleterefernce

learn-ethical-hacking-from-scratch

Learn Ethical Hacking from Scratch, published by Packt Publishing

linearalgebra

My exercises of - Discover the Mathematical Language of Data in Python - Basics of Linear Algebra for Machine Learning by Jason Brownlee

matlab-machine-learning

Source Code for 'MATLAB Machine Learning' by Michael Paluszek and Stephanie Thomas

mental-disorders

ml-algorithms-in-matlab

MATLAB Code for Linear & Logistic Regression, SVM, K Means and PCA, Neural Networks Learning, Multiclass Classification, Anomaly Detection and Recommender systems.

ml-lstm-stockprediction

ML(LSTM)StockPrediction

rice-quality-analysis

Classify rice grains by calculating their average length/breadth ratio by using Image processing in python.

sheffieldhallam-pgr-template

LaTeX template for PGR final document (thesis) for Sheffield Hallam University research students.

udacity-machine-learning-nanodegree

All projects and lecture notes of the Udacity Machine Learning Engineer Nanodegree.

udacity-natural-language-processing-nanodegree

Tutorials and my solutions to the Udacity NLP Nanodegree