Skip to main content

Privacy Policy

The privacy of our visitors to machinelearningforbeginner.blogspot.com is important to us.
At machinelearningforbeginner.blogspot.com, we recognize that privacy of your personal information is important. Here is information on what types of personal information we receive and collect when you use and visit machinelearningforbeginner.blogspot.com, and how we safeguard your information. We never sell your personal information to third parties.
Log Files
As with most other websites, we collect and use the data contained in log files. The information in the log files include your IP (internet protocol) address, your ISP (internet service provider, such as AOL or Shaw Cable), the browser you used to visit our site (such as Internet Explorer or Firefox), the time you visited our site and which pages you visited throughout our site.

Cookies and Web Beacons

We do use cookies to store information, such as your personal preferences when you visit our site. This could include only showing you a popup once in your visit, or the ability to login to some of our features, such as forums.
We also use third party advertisements on TechSkeech.com to support our site.
Some of these advertisers may use technology such as cookies and web beacons when they advertise on our site, which will also send these advertisers (such as Google through the Google AdSense program) information including your IP address, your ISP , the browser you used to visit our site, and in some cases, whether you have Flash installed.
This is generally used for geotargeting purposes (showing New York real estate ads to someone in New York, for example) or showing certain ads based on specific sites visited (such as showing cooking ads to someone who frequents cooking sites).
You can chose to disable or selectively turn off our cookies or third-party cookies in your browser settings, or by managing preferences in programs such as Norton Internet Security.
However, this can affect how you are able to interact with our site as well as other websites. This could include the inability to login to services or programs, such as logging into forums or accounts.

Comments

Post a Comment

Popular posts from this blog

IP camera access through python

In this tutorial we access IP camera using python. from urllib.request import Request, urlopen import base64 import cv2 import urllib import numpy as np url = 'http://192.168.0.104:8080/shot.jpg' username = '' password = '' while True:     proxy_handler = urllib.request.ProxyHandler({})     opener = urllib.request.build_opener(proxy_handler)     imgResp = Request(url, headers={"User-Agent": "Mozilla/5.0"})     base64string = base64.b64encode(('%s:%s' % (username, password)).encode("utf-8")).decode("utf-8")     imgResp.add_header("Authorization", "Basic %s" % base64string)     r = opener.open(imgResp)     imgNp = np.array(bytearray(r.read()), dtype=np.uint8)     img = cv2.imdecode(imgNp, -1)     cv2.imshow('test', img)     if ord('q') == cv2.waitKey(10):         exit(0)     # all the opencv processing is done here     cv2.imshow('test', img)     if o
Post a Comment
Read more

Simple linear regression model with scikit-learn

Simple Leaner Regression Model is use to find the relation ship between two variable. It is commonly used in the predict analysis. Suppose we want to know price of pizza on the basis of size. We will train a model on the different size of pizza and its price. Then we will give the size of the pizza to train model it will predict its price. suppose we have different size of pizza x =  [[6], [8], [10], [14], [18]]] and its price y = [[7], [9], [13], [17.5], [18]]. Let's implement this problem it scikit-learn. Firs import Linear Regress from scikit-learn pakage. from sklearn.linear_model import LinearRegression Import Numpy module because when b give the data to model it will only accept if the data in Numpy array. from sklearn.linear_model import LinearRegression import numpy as np Import matplot libraray which use to Draw a plot of our data import matplotlib.pyplot as plt x = [[6], [8], [10], [14], [18]] x = np.reshape(x, (-1, 1)) Her we rehshape array to 2d because it ac
Post a Comment
Read more

How parse XML file Dataset using python

Parse XML file and Store data in CSV file for machine learning Algorithms. import xml.etree.ElementTree as ET import os import csv path = 'G:\salman' with open('names.csv', 'a') as csvfile:     fieldnames = ['pair_id', 'e1', 'e2', 'Sentance']     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)     for filename in os.listdir(path):         if not filename.endswith('.xml'): continue         fullname = os.path.join(path, filename)         tree = ET.parse(fullname)         lst = tree.findall('sentence')         for i in lst:             i_ = i.findall('pair')             for elem in i_:                 if elem.attrib['ddi'] == 'true':                     writer.writerow({'pair_id': elem.attrib['id'], 'e1': elem.attrib['e1'], 'e2': elem.attrib['e2'], 'Sentance': i.attrib["text"]})
Post a Comment
Read more