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Introduction about CAPTCHA.

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What is CAPTCHA?

A CAPTCHA acronym - Completely Automated Public Turing test to tell Computers and Humans Apart

CAPTCHA is a type of challenge-response test used in computing to determine whether or not the user is human.

A CAPTCHA differentiates between human and bot by setting some task that is easy for most humans to perform but is more difficult and time-consuming for current bots to complete.

CAPTCHAs are often used to stop bots and other automated programs.

Sample Example for CAPTCHA


1)Distinguishes between a human and a machine
2)Makes online polls more legitimate
3)Reduces spam and viruses
4)Makes online shopping safer
5)Diminishes abuse of free email account services


1)Sometimes very difficult to read
2)Are not compatible with users with disablilities
3)Time-consuming to decipher
4)Technical difficulties with certain internet browsers
5)May greatly enhance Artificial Intelligence

Video for What is Captcha

posted Nov 27, 2014 by Ujjwal Mehra

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CAPTCHA is like artificial intelligence service provided for the online security purpose.
Captcha code is available in both numerical and alphabetic way. If any user accesses some site and edits some information in the websites, captcha code is used for the security  purpose so that customer can easily access the websites. While the captcha code is not matched the required code than the customer can't able to access the website.

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What is aiohttp?
Asynchronous HTTP client/server framework for asyncio and Python 


  • Supports both client and server side of HTTP protocol.
  • Supports both client and server Web-Sockets out-of-the-box and avoids Callback Hell.
  • Provides Web-server with middlewares and pluggable routing.


pip install aiohttp

You may want to install optional cchardet library as faster replacement for chardet:

pip install cchardet

For speeding up DNS resolving by client API you may install aiodns as well. This option is highly recommended:

pip install aiodns


import aiohttp
import asyncio

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        html = await fetch(session, '')

if __name__ == '__main__':
    loop = asyncio.get_event_loop()

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What is Seaborn?
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.


  • A dataset-oriented API for examining relationships between multiple variables
  • Specialized support for using categorical variables to show observations or aggregate statistics
  • Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
  • Automatic estimation and plotting of linear regression models for different kinds dependent variables
  • Convenient views onto the overall structure of complex datasets
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  • Tools for choosing color palettes that faithfully reveal patterns in your data

Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.

Example Code

import seaborn as sns
tips = sns.load_dataset("tips")
sns.relplot(x="total_bill", y="tip", col="time",
            hue="smoker", style="smoker", size="size",

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What is Mlpack Library?

mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. 

This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.

As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.

mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)

mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.

mlpack bindings for R are provided by the RcppMLPACK project.

Currently mlpack supports the following algorithms:

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  • Decision stumps (one-level decision trees)
  • Density Estimation Trees
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  • Principal Components Analysis (PCA)
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For more detail visit here -

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What is PyShark?

PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!

Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.

There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.

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Example Code for Reading a File

import pyshark
cap = pyshark.FileCapture('/tmp/mycapture.cap')
>>> <FileCapture /tmp/mycapture.cap>
print cap[0]
Packet (Length: 698)
Layer ETH:
        Destination: aa:bb:cc:dd:ee:ff
        Source: 00:de:ad:be:ef:00
        Type: IP (0x0800)
Layer IP:
        Version: 4
        Header Length: 20 bytes
        Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport))
        Total Length: 684
        Identification: 0x254f (9551)
        Flags: 0x00
        Fragment offset: 0
        Time to live: 1
        Protocol: UDP (17)
        Header checksum: 0xe148 [correct]

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What is FastText?

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Steps for Installing

- git clone
- cd fastText
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Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.

What is text classification?
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Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

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