MAR 05 2017

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Sep 23 2017

Packt Publishing - Finding and Exploiting Hidden Vulnerabilities

Packt Publishing - Finding and Exploiting Hidden Vulnerabilities

Packt Publishing - Finding and Exploiting Hidden Vulnerabilities
English | Size: 764.89 MB
Category: Tutorial

When learning about penetration testing, vulnerability assessments play an important role, followed by exploiting the vulnerabilities identified. Finally, everything needs to be documented properly to present to the client. This course focuses on all these elements of the VAPT activity.

Courses focused on teaching Vulnerability Assessment and Penetration Testing sometimes miss out on essential elements such as documentation. Keeping this in mind, the course module has been developed to help you understand the process of VAPT step by step. So here you’ll learn how to perform vulnerability assessments using well-known softwares such as Nessus and OpenVas.
Sep 23 2017

Packt Publishing - Practical Python Data Science Techniques

Packt Publishing - Practical Python Data Science Techniques

Packt Publishing - Practical Python Data Science Techniques
English | Size: 489.80 MB
Category: Tutorial

Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise.

This course takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. This video will begin from exploring your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. It will show how to deal with text using different methods like text normalization and calculating word frequencies. The audience will learn how to deal with data with a time dimension and how to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). They will learn how to perform text preprocessing steps that are necessary for every text analysis applications. Specifically, the course will cover tokenization, stop-word removal, stemming and other preprocessing techniques.
Sep 23 2017

Packt Publishing - Kotlin Fundamentals

Packt Publishing - Kotlin Fundamentals

Packt Publishing - Kotlin Fundamentals
English | Size: 743.40 MB
Category: Tutorial

Kotlin is an easy-to-learn, pragmatic language that has adopted the best traits of other popular languages such as Java, C#, Python, Groovy, and Scala. This video tutorial is your one stop guide to getting started with Kotlin programming, covering the basics, object-oriented features, and even functional programming in Kotlin.

You’ll take your first steps in Kotlin programming by building a simple “Hello, Kotlin” program. You’ll explore the basics such as types and syntax, and also basics of object-oriented programming such as classes and inheritance. Next, you’ll dive into Kotlin objects and functions, and see how they’re different from other programming languages. You’ll learn to organize your code and simplify your coding experience with the help of different functions and statements in Kotlin. From there, you’ll lwork with collections and streams, employ loops in your code, and filter data with the help of different collection operations.
Sep 23 2017

Packt Publishing - Making Predictions with Data and Python

Packt Publishing - Making Predictions with Data and Python

Packt Publishing - Making Predictions with Data and Python
English | Size: 771.65 MB
Category: Tutorial

Python has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python.

During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets.
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