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Podcasts
Learn Ansible. Automate cloud, security, and network infrastructure using Ansible 2.x
Ansible has grown from a small, open source orchestration tool to a full-blown orchestration and configuration management tool owned by Red Hat. Its powerful core modules cover a wide range of infrastructures, including on-premises systems and public clouds, operating systems, devices, and services—meaning it can be used to manage pretty much your entire end-to-end environment. Trends and surveys say that Ansible is the first choice of tool among system administrators as it is so easy to use.This end-to-end, practical guide will take you on a learning curve from beginner to pro. You'll start by installing and configuring the Ansible to perform various automation tasks. Then, we'll dive deep into the various facets of infrastructure, such as cloud, compute and network infrastructure along with security.By the end of this book, you'll have an end-to-end understanding of Ansible and how you can apply it to your own environments.
R Data Mining. Implement data mining techniques through practical use cases and real-world datasets
R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Securing Network Infrastructure. Discover practical network security with Nmap and Nessus 7
Digitization drives technology today, which is why it’s so important for organizations to design security mechanisms for their network infrastructures. Analyzing vulnerabilities is one of the best ways to secure your network infrastructure.This Learning Path begins by introducing you to the various concepts of network security assessment, workflows, and architectures. You will learn to employ open source tools to perform both active and passive network scanning and use these results to analyze and design a threat model for network security. With a firm understanding of the basics, you will then explore how to use Nessus and Nmap to scan your network for vulnerabilities and open ports and gain back door entry into a network. As you progress through the chapters, you will gain insights into how to carry out various key scanning tasks, including firewall detection, OS detection, and access management to detect vulnerabilities in your network.By the end of this Learning Path, you will be familiar with the tools you need for network scanning and techniques for vulnerability scanning and network protection.This Learning Path includes content from the following Packt books:•Network Scanning Cookbook by Sairam Jetty•Network Vulnerability Assessment by Sagar Rahalkar
Revathi Gopalakrishnan, Avinash Venkateswarlu
Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples.You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains.By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
This book has extensive coverage of techniques that will help prevent attackers from breaching your system, by building a much more secure Linux environment. You will learn various security techniques such as SSH hardening, network service detection, setting up firewalls, encrypting file systems, protecting user accounts, authentication processes, and so on. Moving forward, you will also develop hands-on skills with advanced Linux permissions, access control, special modes, and more. Lastly, this book will also cover best practices and troubleshooting techniques to get your work done efficiently.By the end of this book, you will be confident in delivering a system that will be much harder to compromise.
Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover
QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations.Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources.With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro.This Learning Path includes content from the following Packt products:• QlikView for Developers by Miguel Ángel García, Barry Harmsen • Mastering QlikView by Stephen Redmond • Mastering QlikView Data Visualization by Karl Pover
Mastering Salesforce CRM Administration. An Advanced Administration Certification Handbook
The book starts by refreshing your knowledge of common admin tasks. You will go over Lightning Experience and various security aspects. You will be shown how to implement territory management in your organization and make use of custom objects.From here, the book progresses to advanced configuration, data management, and data analytics before swiftly moving on to setting up advanced organization-wide features that affect the look and feel of the application. Extend the capabilities of your organization’s Salesforce implementation by optimizing and extending Sales Cloud, Salesforce1, and Service Cloud applications. This guide will equip you with the tools you need to build a successful career in Salesforce.
MATLAB for Machine Learning. Practical examples of regression, clustering and neural networks
MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.