Python Applications Courses Online

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Python Applications Training


Learn Python Neural Network Classification

About

Python-Neural-Network-Classification

Python Neural Network that classifies through the training database the type of flower (Iris). This code example is from a Python classifying neural network with 3 different outputs, which informs which type of (Iris) out of the three is correct, taking into account the database used for training. The database presents three types of (Iris) which can be (Iris setosa), (Iris virginica) or (Iris versicolor). As for the Neural network, it has 4 inputs, two hidden layers with 8 neurons each, an output layer with three neurons, normal weight initialization function, 'relu' and 'softmax' activation function, and uses as optimizer the ' Adam '. Curiosity: To improve the neural network one has to test several values that the 'Keras' library offers, so it is advisable to study cross-validation.


7 hours

$1,990

Explore AuthID

About

AuthID

A Python3 program to identify the author of an unknown text. This is done by analysing the charactertistic ngram frequencies of the authors' works in the training set, and the matched to data in the test set.

Prerequisites

You need python3 and nltk installed. Further, there should be 2 directories - "Train Data" and "Test Data", present in the directory where the py file is located.


7 hours

$1,990

Discover Pycoach

About

PyCoach: a training package for PyTorch

PyCoach is a Python package that provides: The main purpose of PyCoach is handle training, evaluation and prediction, leaving other tasks like create the network model and dataloaders to the users. This is an initial personal project and contribution to it is appreciated. Actual status: Working with PyTorch 0.4!

To Do:


7 hours

$1,990

Learn PythonFundamentals

About

Python Fun(damentals)

Course TOC

The course is built to be taught in a structured order: 1) Python 3 Setup 2) Python 3 Core 3) Python 3 Types 4) Python 3 Variables 5) Python 3 Flow Control

Course Introduction

This course was designed in free time for our team who are not already or currently studying basic programing principles. It is built to introduce them to a few concepts that are key to understanding the Python programing language and syntax. Many of the lessons as a seasoned programmer come only with time, this course is meant to arm a programmer with the thought process and how logic works within the programing world and the basic concepts to apply that thought process to get Python to do what you need it get the job done.


7 hours

$1,990

Learn Peepdf

About

peepdf is a Python tool to explore PDF files in order to find out if the file can be harmful or not. The aim of this tool is to provide all the necessary components that a security researcher could need in a PDF analysis without using 3 or 4 tools to make all the tasks. With peepdf it's possible to see all the objects in the document showing the suspicious elements, supports all the most used filters and encodings, it can parse different versions of a file, object streams and encrypted files. With the installation of PyV8 and Pylibemu it provides Javascript and shellcode analysis wrappers too. Apart of this it's able to create new PDF files and to modify/obfuscate existent ones. The main functionalities of peepdf are the following: Analysis:

  • Decodings: hexadecimal, octal, name objects
  • More used filters
  • References in objects and where an object is referenced
  • Strings search (including streams)
  • Physical structure (offsets)
  • Logical tree structure
  • Metadata
  • Modifications between versions (changelog)
  • Compressed objects (object streams)
  • Analysis and modification of Javascript (PyV8): unescape, replace, join
  • Shellcode analysis (Libemu python wrapper, pylibemu)
  • Variables (set command)
  • Extraction of old versions of the document
  • Easy extraction of objects, Javascript code, shellcodes (>, >>, $>, $>>)
  • Checking hashes on VirusTotal Creation/Modification:
  • Basic PDF creation
  • Creation of PDF with Javascript executed wen the document is opened
  • Creation of object streams to compress objects
  • Embedded PDFs
  • Strings and names obfuscation
  • Malformed PDF output: without endobj, garbage in the header, bad header...
  • Filters modification
  • Objects modification Execution modes:
  • Simple command line execution
  • Powerful interactive console (colorized or not)
  • Batch mode TODO:
  • Embedded PDFs analysis
  • Improving automatic Javascript analysis
  • GUI Related articles:
  • Spammed CVE-2013-2729 PDF exploit dropping ZeuS-P2P/Gameover
  • New peepdf v0.2 (Version Black Hat Vegas 2012)
  • peepdf supports CCITTFaxDecode encoded streams
  • Explanation of the changelog of peepdf for Black Hat Europe Arsenal 2012
  • How to extract streams and shellcodes from a PDF, the easy way
  • Static analysis of a CVE-2011-2462 PDF exploit
  • Analysis of a malicious PDF from a SEO Sploit Pack
  • Analysing the Honeynet Project challenge PDF file with peepdf Part 1 Part 2
  • Analyzing Suspicious PDF Files With Peepdf Included in:
  • REMnux
  • BackTrack 5
  • Kali Linux You are free to contribute with feedback, bugs, patches, etc. Any help is welcome. 

7 hours

$1,990

Learn Pyjanitor

About

pyjanitor

pyjanitor is a Python implementation of the R package janitor, and provides a clean API for cleaning data. Why janitor? Originally a port of the R package, pyjanitor has evolved from a set of convenient data cleaning routines into an experiment with the method chaining paradigm. chaining Data preprocessing usually consists of a series of steps that involve transforming raw data into an understandable/usable format. These series of steps need to be run in a certain sequence to achieve success. We take a base data file as the starting point, and perform actions on it, such as removing null/empty rows, replacing them with other values, adding/renaming/removing columns of data, filtering rows and others. More formally, these steps along with their relationships and dependencies are commonly referred to as a Directed Acyclic Graph (DAG). The pandas API has been invaluable for the Python data science ecosystem, and implements method chaining of a subset of methods as part of the API. For example, resetting indexes (.reset_index()), dropping null values (.dropna()), and more, are accomplished via the appropriate pd.DataFrame method calls. Inspired by the ease-of-use and expressiveness of the dplyr package of the R statistical language ecosystem, we have evolved pyjanitor into a language for expressing the data processing DAG for pandas users. To accomplish this, actions for which we would need to invoke imperative-style statements, can be replaced with method chains that allow one to read off the logical order of actions taken. Let us see the annotated example below. First off, here is the textual description of a data cleaning pathway:

  1. Create a DataFrame.
  2. Delete one column.
  3. Drop rows with empty values in two particular columns.
  4. Rename another two columns.
  5. Add a new column. Let's import some libraries and begin with some sample data for this example :

    Libraries

    import numpy as np import pandas as pd import janitor

    Sample Data curated for this example

    company_sales = { 'SalesMonth': ['Jan', 'Feb', 'Mar', 'April'], 'Company1': [150.0, 200.0, 300.0, 400.0], 'Company2': [180.0, 250.0, np.nan, 500.0], 'Company3': [400.0, 500.0, 600.0, 675.0] } In pandas code, most users might type something like this:

    The Pandas Way

    1. Create a pandas DataFrame from the company_sales dictionary

    df = pd.DataFrame.from_dict(company_sales)

    2. Delete a column from the DataFrame. Say 'Company1'

    del df['Company1']

    3. Drop rows that have empty values in columns 'Company2' and 'Company3'

    df = df.dropna(subset=['Company2', 'Company3'])

    4. Rename 'Company2' to 'Amazon' and 'Company3' to 'Facebook'

    df = df.rename( { 'Company2': 'Amazon', 'Company3': 'Facebook', }, axis=1, )

    5. Let's add some data for another company. Say 'Google'

    df['Google'] = [450.0, 550.0, 800.0]

    Output looks like this:

    Out[15]:

    SalesMonth Amazon Facebook Google

    0 Jan 180.0 400.0 450.0

    1 Feb 250.0 500.0 550.0

    3 April 500.0 675.0 800.0

    Slightly more advanced users might take advantage of the functional API: df = ( pd.DataFrame(company_sales) .drop(columns="Company1") .dropna(subset=['Company2', 'Company3']) .rename(columns={"Company2": "Amazon", "Company3": "Facebook"}) .assign(Google=[450.0, 550.0, 800.0]) )

    Output looks like this:

    Out[15]:

    SalesMonth Amazon Facebook Google

    0 Jan 180.0 400.0 450.0

    1 Feb 250.0 500.0 550.0

    3 April 500.0 675.0 800.0

    With pyjanitor, we enable method chaining with method names that are verbs, which describe the action taken. df = ( pd.DataFrame.from_dict(company_sales) .remove_columns(['Company1']) .dropna(subset=['Company2', 'Company3']) .rename_column('Company2', 'Amazon') .rename_column('Company3', 'Facebook') .add_column('Google', [450.0, 550.0, 800.0]) )

    Output looks like this:

    Out[15]:

    SalesMonth Amazon Facebook Google

    0 Jan 180.0 400.0 450.0

    1 Feb 250.0 500.0 550.0

    3 April 500.0 675.0 800.0

    As such, pyjanitor's etymology has a two-fold relationship to "cleanliness". Firstly, it's about extending Pandas with convenient data cleaning routines. Secondly, it's about providing a cleaner, method-chaining, verb-based API for common pandas routines.


7 hours

$1,990

Know CORScanner

About

About CORScanner

CORScanner is a python tool designed to discover CORS misconfigurations vulnerabilities of websites. It helps website administrators and penetration testers to check whether the domains/urls they are targeting have insecure CORS policies.

Features

  • Fast. It uses gevent instead of Python threads for concurrency, which is much faster for network scanning.
  • Comprehensive. It covers all the common types of CORS misconfigurations we know.
  • Flexible. It supports various self-define features (e.g. file output), which is helpful for large-scale scanning. Two useful references for understanding CORS systematically:
  • USENIX security 18 paper: We Still Dont Have Secure Cross-Domain Requests: an Empirical Study of CORS
  • SOPCORS

    Screenshots


7 hours

$1,990

Discover Aelius Brazilian Portuguese POS Tagger

About

Python, NLTK-based package for shallow parsing of Brazilian Portuguese

Aelius is an ongoing open source project aiming at developing a suite of Python, NLTK-based modules and interfaces to external freely available tools for shallow parsing of Brazilian Portuguese. It also includes language resources such as language models, sample texts, and gold standards. Presently, Aelius already offers facilities for POS-tagging and chunking corpora and outputting annotations in different formats, such as in XML in the TEI P5 encoding scheme.


7 hours

$1,990

Learn 2048 Python AI 1 or 2 players

About

py 2048, a well know 2048 clone board game

Written in Python 2.7.x and pygame1.9.1~2 compliant (at least) Python 3.4 HOW TO PLAY: keyboard UP, DOWN, LEFT, RIGHT, escape to quit increase your score shifting tiles with same number. the value of resulting tile is multiplied *2. CUSTOMISE YOUR GAME: edit py2048.cfg configuration file. you can: -Change Raws and Columns amount. -Change Random range value of new tile. -Limit the maximum tile value. -Limit time allowed for movement. -Enable no movement (a new tile appear even if your movement doesn't shift anything). -Set shift to one step only. -Save the game state when exit (for autoload at start game) -Key config player 2 ect... V0.1.0: -add Small, Medium and Large preconfig Panel Game. -add HiScore for Small, Medium And Large Panel Game -check for close windows as QUIT event. -add Change theme screen BckGnd on score tile. -add counter move V0.1.2: -compliant Pyhton34 -add AI Auto Boot -add 2 players Human/AI (keys A,Q,W,X)


7 hours

$1,990

Discover 2048 Python AI 1 or 2 players

About

py 2048, a well know 2048 clone board game

Written in Python 2.7.x and pygame1.9.1~2 compliant (at least) Python 3.4 HOW TO PLAY: keyboard UP, DOWN, LEFT, RIGHT, escape to quit increase your score shifting tiles with same number. the value of resulting tile is multiplied *2. CUSTOMISE YOUR GAME: edit py2048.cfg configuration file. you can: -Change Raws and Columns amount. -Change Random range value of new tile. -Limit the maximum tile value. -Limit time allowed for movement. -Enable no movement (a new tile appear even if your movement doesn't shift anything). -Set shift to one step only. -Save the game state when exit (for autoload at start game) -Key config player 2 ect... V0.1.0: -add Small, Medium and Large preconfig Panel Game. -add HiScore for Small, Medium And Large Panel Game -check for close windows as QUIT event. -add Change theme screen BckGnd on score tile. -add counter move V0.1.2: -compliant Pyhton34 -add AI Auto Boot -add 2 players Human/AI (keys A,Q,W,X)


7 hours

$1,990

Fundamentals of PyVcon

About

A stylish Video Converter written in Python

PyVcon is a Python video converter using PyQt as its primary GUI Toolkit and because of this, PyVcon has a very sleek user friendly interface. Using ffmpeg for video conversion, PyVcon has great performance in speed and converts any kind of video into mp4, mkv, wmv, avi, 3gp, m4a, mp3 and wma formats. Also included, is MediaInfo who PyVcon partly depends for video metadata generation. PyVcon is also generally known to be of outstanding value, possessing a highly flexible video quality control feature, PyVcon gives you full control over your video's final appearance and size. One of the most special and unique features of PyVcon is its inbuilt ability to stream-copy any kind of video added with a special ability to make known to the user what happens in the background by printing out stdout and stderr streams directly from ffmpeg unto the GUI in real time. You can also output this stdout and stderr streams as a text file for further analysis and inspection.


7 hours

$1,990

Explore BlockIt

About

BlockIt provides a Python framework to scan and parse a program file into constituent nested blocks, however defined, forming a block tree of your code and can be used as a mechanism to "extend" in some sense, the underlying programming language.


7 hours

$1,990

Discover Daft Shell

About

An experimental terminal shell project in Python & curses. The aim is to be a command-driven language, with simple english-like grammar but still powerful and easy to do most CLI tasks without having to remember "weird" unix commands.


7 hours

$1,990

Know Limited Shell lshell

About

lshell is a shell coded in Python, that lets you restrict a user's environment to limited sets of commands, choose to enable/disable any command over SSH (e.g. SCP, SFTP, rsync, etc.), log user's commands, implement timing restriction, and more.


7 hours

$1,990


Is learning Python Applications hard?


In the field of Python Applications learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.


Is Python Applications a good field?


For now, there are tremendous work opportunities for various IT fields. Most of the courses in Python Applications is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.



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