Abraham Zamudio Chauca robintux

## ejemplo_html_code.html
<!DOCTYPE html>
<html>
<body>

<h1>My First Heading</h1>
<h3>My Third Heading</h3>
<div id ="names">
    <ul>
        <li>Liam James</li>
        <li>William Jones</li>

## MostrarValores.py
def show_values(axs, orient="v", space=.01):
    def _single(ax):
        if orient == "v":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height() + (p.get_height()*0.01)
                value = '{:.1f}'.format(p.get_height())
                ax.text(_x, _y, value, ha="center")
        elif orient == "h":
            for p in ax.patches:

## visualizar_clasificador.py
def visualizar_clasificador(clasificador, X, y):
    #definimos los máximos valores de X e y para la malla
    min_x, max_x = X[:, 0].min() - 1.0, X[:, 0].max() + 1.0
    min_y, max_y = X[:, 1].min() - 1.0, X[:, 1].max() + 1.0
    #definimos el paso de la malla
    mesh_step_size = 0.01
    #definimos la malla para x e y
    x_vals, y_vals = np.mgrid[min_x:max_x:mesh_step_size, min_y:max_y:mesh_step_size]
    #corremos el clasificador sobre la malla
    resultados = clasificador.predict(np.c_[x_vals.ravel(), y_vals.ravel()])

## repasopython.ipynb

      
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                robintux
                / repasopython.ipynb
            
            
              Last active
              November 26, 2021 22:02
            
              
                RepasoPython.ipynb
              
          
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## Data4SeS.py

data = [
    446.6565,
    454.4733,
    455.663,
    423.6322,
    456.2713,
    440.5881,
    425.3325,
    485.1494,

## get_etf_holdings.py
# Implementamos un par de funciones que nos permitan obtener la composicion de ETFs

from bs4 import BeautifulSoup
import matplotlib
import matplotlib.pyplot as plt
import re
import requests
import numpy as np
import pandas as pd

## welcome-to-machine-learning-with-colaboratory.ipynb

      
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                robintux
                / welcome-to-machine-learning-with-colaboratory.ipynb
            
            
              Created
              May 6, 2021 14:08
                — forked from craftoid/welcome-to-machine-learning-with-colaboratory.ipynb
            
              
                Welcome to Machine Learning with Colaboratory
              
          
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## script02.R
sessionInfo()
library(help= datasets)

#### ggplot2 ####
library(ggplot2)
library(car)

#### cargamos datos ####
data(Salaries)
class(Salaries)

## script01.R


#### Primeros pasos ####
library(quantmod)
library(help= quantmod)

#### carguemos datos financieros ####
help(getSymbols)

# descarguemos los precios de la empresa IBM

## CharlaCTIC_NoSupervisado.ipynb

      
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                robintux
                / CharlaCTIC_NoSupervisado.ipynb
            
            
              Created
              October 16, 2020 03:42
            
              
                Charla : Aprendizaje no Supervisado (15/10/2020)
              
          
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	<!DOCTYPE html>
	<html>
	<body>

	<h1>My First Heading</h1>
	<h3>My Third Heading</h3>
	<div id ="names">
	<ul>
	<li>Liam James</li>
	<li>William Jones</li>
	def show_values(axs, orient="v", space=.01):
	def _single(ax):
	if orient == "v":
	for p in ax.patches:
	_x = p.get_x() + p.get_width() / 2
	_y = p.get_y() + p.get_height() + (p.get_height()*0.01)
	value = '{:.1f}'.format(p.get_height())
	ax.text(_x, _y, value, ha="center")
	elif orient == "h":
	for p in ax.patches:
	def visualizar_clasificador(clasificador, X, y):
	#definimos los máximos valores de X e y para la malla
	min_x, max_x = X[:, 0].min() - 1.0, X[:, 0].max() + 1.0
	min_y, max_y = X[:, 1].min() - 1.0, X[:, 1].max() + 1.0
	#definimos el paso de la malla
	mesh_step_size = 0.01
	#definimos la malla para x e y
	x_vals, y_vals = np.mgrid[min_x:max_x:mesh_step_size, min_y:max_y:mesh_step_size]
	#corremos el clasificador sobre la malla
	resultados = clasificador.predict(np.c_[x_vals.ravel(), y_vals.ravel()])

	data = [
	446.6565,
	454.4733,
	455.663,
	423.6322,
	456.2713,
	440.5881,
	425.3325,
	485.1494,
	# Implementamos un par de funciones que nos permitan obtener la composicion de ETFs

	from bs4 import BeautifulSoup
	import matplotlib
	import matplotlib.pyplot as plt
	import re
	import requests
	import numpy as np
	import pandas as pd
	sessionInfo()
	library(help= datasets)

	#### ggplot2 ####
	library(ggplot2)
	library(car)

	#### cargamos datos ####
	data(Salaries)
	class(Salaries)


	#### Primeros pasos ####
	library(quantmod)
	library(help= quantmod)

	#### carguemos datos financieros ####
	help(getSymbols)

	# descarguemos los precios de la empresa IBM