lfcunha

import math
balance=10050000
annualInterestRate=0.2



monthly=annualInterestRate / 12
lo=balance/12
hi=balance+(balance * annualInterestRate)

lfcunha

from numpy import loadtxt, zeros, ones, array, linspace, logspace, mean, std, arange
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pylab import plot, show, xlabel, ylabel

#Evaluate the linear regression

def feature_normalize(X):
    '''
    Returns a normalized version of X where

lfcunha

from numpy import loadtxt, zeros, ones, array, linspace, logspace
from pylab import scatter, show, title, xlabel, ylabel, plot, contour


#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples

lfcunha

Keybase proof

I hereby claim:

• I am lfcunha on github.
• I am lfcunha (https://keybase.io/lfcunha) on keybase.
• I have a public key whose fingerprint is 0AFA BDD9 265E 0FC8 CE1C 948B EB98 2777 99A7 9168

To claim this, I am signing this object:

lfcunha

__author__ = "Luis C"
__date__ = "09_24_2015"

"""
Data Analysis and Interpretation Specialization
First course: Data Management and Visualization

Week 2: Write your first program

"""

lfcunha

__author__ = 'luiscunha'

# -*- coding: utf-8 -*-
"""
Created on 10/03/2015

@author: Luis Cunha
"""

import pandas

lfcunha

How to setup Let's Encrypt for Nginx on Ubuntu 16.04 (including IPv6, HTTP/2 and A+ SLL rating)

There are two main modes to run the Let's Encrypt client (called Certbot):

• Standalone: replaces the webserver to respond to ACME challenges
• Webroot: needs your webserver to serve challenges from a known folder.

Webroot is better because it doesn't need to replace Nginx (to bind to port 80).

In the following, we're setting up mydomain.com. HTML is served from /var/www/mydomain, and challenges are served from /var/www/letsencrypt.

lfcunha

openapi: "3.0.1"
info:
  title: "tovanahealth"
  version: "2018-11-29T06:50:41Z"
servers:
- url: "https://api.tovanahealth.com/{basePath}"
  variables:
    basePath:
      default: "/ftdna"
paths:

lfcunha

import json
import requests
from requests_aws4auth import AWS4Auth

aws_key = '<CHANGEME>'
aws_secret = '<CHANGEME>'
region = 'us-east-2'
aws_service = 'execute-api'
api_endpoint = 'https://api.tovanahealth.com/ftdna/kit'

lfcunha

Movies Recommendation:

• MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
• Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
• Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
• Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

• Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html