Pinned Repositories
action-hosting-deploy
Automatically deploy shareable previews for your Firebase Hosting sites
aws-cognito-idp-userpool-domain
:zap: Manage aws cognito userpool hosted domain with Serverless Framework
centos7-quicksetup
a personal collection of useful script to build my machine after a fresh install
cloudconfig
A simple Config Server (spring cloud) reference implementation
cosinsimilarity
a cosine similarity recommender engine
mailserver-quicksetup
Have your own mailtoaster - A script to automate all steps neded to install https://github.com/hardware/mailserver on a digitalocean droplet
reactive-microservices
under construction - to showcase CQRS and Reative microservices with Reactor
restgraph
Simple Rest api with angularjs and neo4j (in 20 minutes)
udacity-mlend-customer_segments
Creating Customer Segments - In this project you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
rubentrancoso's Repositories
rubentrancoso/aws-cognito-idp-userpool-domain
:zap: Manage aws cognito userpool hosted domain with Serverless Framework
rubentrancoso/centos7-quicksetup
a personal collection of useful script to build my machine after a fresh install
rubentrancoso/udacity-mlend-customer_segments
Creating Customer Segments - In this project you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
rubentrancoso/action-hosting-deploy
Automatically deploy shareable previews for your Firebase Hosting sites
rubentrancoso/reactive-microservices
under construction - to showcase CQRS and Reative microservices with Reactor
rubentrancoso/automenu
Automenu
rubentrancoso/challenge
rubentrancoso/challenge-flutter
rubentrancoso/challenge-go
rubentrancoso/challenge-node
same java challenge app written in node
rubentrancoso/digital-bank
digital bank playground
rubentrancoso/fastbook
Draft of the fastai book
rubentrancoso/Grokking-Artificial-Intelligence-Algorithms
The official code repository supporting the book, Grokking Artificial Intelligence Algorithms
rubentrancoso/IBM-Data-Science
Respository of the practical assigments of the course IBM Data Science from coursera
rubentrancoso/ibm_data_analisys_professional_cert
rubentrancoso/InfiniteCanvas
rubentrancoso/katacoda-scenarios
Katacoda Scenarios
rubentrancoso/log-parser
rubentrancoso/maratona-ibm-desafio-1-2020
rubentrancoso/oracle-docker
simple scripts to initialize and run a Oracle instance locally - good for dev
rubentrancoso/performance-testing
POC Performance Testing Proxy intercept and Record Data - MockServer
rubentrancoso/plugins
Serverless Plugins – Extend the Serverless Framework with these community driven plugins –
rubentrancoso/rancher-lets-encrypt
Automatically create and manage certificates in Rancher using Let's Encrypt webroot verification via a minimal service
rubentrancoso/serverless-cloudformation-changesets
Natively deploy to CloudFormation via Change sets, instead of directly. Allowing you to queue changes, and safely require escalated roles for final deployment.
rubentrancoso/ses-auto-forward-by-language
rubentrancoso/udacity-mlaif-bostonhousing
Boston Housing project for Machine Learning & AI Foundations course from Udacity
rubentrancoso/udacity-mlend-finding_donors
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Your goal will be evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.
rubentrancoso/vanhackfair2-0
rubentrancoso/webcamcapture
rubentrancoso/writing