Pentester Academy Python For Pentesters

Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms.

You've tried to figure out what all those technical terms are all about (WTF is a reducer??) and you may even be convinced that you need a degree in Computer Science to be able to use Redux. You don't.

Whether you’re a programmer with little to no knowledge of Python, or an experienced data scientist or engineer, this Learning Path will walk you through natural language processing, using both Python and Scala, and show you how to implement a range of popular tools including Spark, scikit-learn, SpaCy, NLTK, and gensim for text mining.

Python from Scratch LiveLessons is a gentler, more entertaining, and more practical starting point to learning the Python programming language. Unlike the typical approach to teaching a programming language that covers each feature section by section, this course is structured more like a cooking class. You can follow along and execute all the code being demonstrated in an interactive notebook as you watch the video, much the same way you would watch a cooking video in your kitchen.

At the end of this course, you will have a thorough understanding of Numpy' s features and when to use them. Numpy is mainly used in matrix computing. We'll do a number of examples specific to matrix computing, which will allow you to see the various scenarios in which Numpy is helpful. There are a few computational computing libraries available for Python. It's important to know when to choose one over the other. Through rigorous exercises, you'll experience where Numpy is powerful and develop and understanding of the scenarios in which Numpy is most useful. You'll also know how to install Numpy.

Computational computing can be a complex topic. How to perform various mathematical functions in code isn't straight forward. With Python's Scipy library, we'll walk through a number of examples showing exactly how to create and execute complex computational computing functions.

Scraping data from webpages can be a tedious job. But it doesn’t have to be. With Scrapy, you can scrape using XPath or CSS. With the large number of examples from both techniques, you’re sure to find a solution that fits for you. Whether your targeting data on a single page or multiple, Scrapy can handle the job. No matter if the data is within a list, you can scrape specific patterns right out of the list. Building up your specific Scrapy job isn't a difficult task. Scrapy is a Python library. If you're familiar with Python, XPath or CSS, you'll feel right at home using Scrapy.

A beginner’s guide to creating your own application with Python

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Make your applications attack-proof by penetration testing with Python