Learn end-to-end automation testing techniques for web and mobile browsers using Selenium WebDriver, AppiumDriver, Java, and TestNG Key Features * Explore the Selenium grid architecture and build your own grid for browser and mobile devices * Use ExtentReports for processing results and SauceLabs for cloud-based test services * Unlock the full potential of Selenium to test your web applications. Book Description Selenium WebDriver 3.x is an open source API for testing both browser and mobile applications. With the help of this book, you can build a solid foundation and can easily perform end-to-end testing on web and mobile browsers.You'll begin by being introduced to the Selenium Page Object Model for software development. You'll architect your own framework with a scalable driver class, Java utility classes, and support for third-party tools and plugins. You'll design and build a Selenium grid from scratch to enable the framework to scale and support different browsers, mobile devices, and platforms.You'll strategize and handle a rich web UI using the advanced WebDriver API and learn techniques to handle real-time challenges in WebDriver. You'll perform different types of testing, such as cross-browser testing, load testing, and mobile testing. Finally, you will also be introduced to data-driven testing, using TestNG to create your own automation framework.By the end of this Learning Path, you'll be able to design your own automation testing framework and perform data-driven testing with Selenium WebDriver. This Learning Path includes content from the following Packt products: * Selenium WebDriver 3 Practical Guide - Second Edition by Unmesh Gundecha * Selenium Framework Design in Data-Driven Testing by Carl Cocchiaro What you will learn * Use different mobile and desktop browser platforms with Selenium 3 * Use the Actions API for performing various keyboard and mouse actions * Design the Selenium Driver Class for local, remote, and third-party grid support * Build page object classes with the Selenium Page Object Model * Develop data-driven test classes using the TestNG framework * Encapsulate data using the JSON protocol * Build a Selenium Grid for RemoteWebDriver testing * Build and use utility classes in synchronization, file I/O, reporting and test listener classes Who this book is for This Learning Path is ideal for software quality assurance/testing professionals, software project managers, or software developers interested in using Selenium for testing their applications. Professionals responsible for designing and building enterprise-based testing frameworks will also find this Learning Path useful. Prior programming experience in Java are TestNG is necessary.

Understand, design, and create cognitive applications using Watson’s suite of APIs. Key Features * Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps * Learn how to build smart apps to carry out different sets of activities using real-world use cases * Get well versed with the best practices of IBM Watson and implement them in your daily work Book Description Cognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows. This book introduces readers to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM Watson APIs. From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic," as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the concept in their respective fields. What you will learn * Get well versed with the APIs provided by IBM Watson on IBM Cloud * Learn ML, AI, cognitive computing, and neural network principles * Implement smart applications in fields such as healthcare, entertainment, security, and more * Understand unstructured content using cognitive metadata with the help of Natural Language Understanding * Use Watson’s APIs to create real-life applications to realize their capabilities * Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is for This book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key Features * Your entry point into the world of artificial intelligence using the power of Python * An example-rich guide to master various RL and DRL algorithms * Explore the power of modern Python libraries to gain confidence in building self-trained applications Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: * Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran * Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani What you will learn * Train an agent to walk using OpenAI Gym and TensorFlow * Solve multi-armed-bandit problems using various algorithms * Build intelligent agents using the DRQN algorithm to play the Doom game * Teach your agent to play Connect4 using AlphaGo Zero * Defeat Atari arcade games using the value iteration method * Discover how to deal with discrete and continuous action spaces in various environments Who this book is for If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.

Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key Features * Learn how to select the most suitable Python library to solve your problem * Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them * Delve into the applications of neural networks using real-world datasets Book Description Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. What you will learn * Understand the basics and importance of clustering * Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages * Explore dimensionality reduction and its applications * Use scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset * Employ Keras to build autoencoder models for the CIFAR-10 dataset * Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data Who this book is for This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.

A comprehensive guide to programming with network sockets, implementing Internet protocols, designing IoT devices, and much more with C Key Features * Leverage your C or C++ programming skills to build powerful network applications * Get to grips with a variety of network protocols that allow you to load web pages, send emails, and do much more * Write portable network code for operating systems such as Windows, Linux, and macOS Book Description Network programming, a challenging topic in C, is made easy to understand with a careful exposition of socket programming APIs. This book gets you started with modern network programming in C and the right use of relevant operating system APIs. This book covers core concepts, such as hostname resolution with DNS, that are crucial to the functioning of the modern web. You’ll delve into the fundamental network protocols, TCP and UDP. Essential techniques for networking paradigms such as client-server and peer-to-peer models are explained with the help of practical examples. You’ll also study HTTP and HTTPS (the protocols responsible for web pages) from both the client and server perspective. To keep up with current trends, you’ll apply the concepts covered in this book to gain insights into web programming for IoT. You’ll even get to grips with network monitoring and implementing security best practices. By the end of this book, you’ll have experience of working with client-server applications, and be able to implement new network programs in C. The code in this book is compatible with the older C99 version as well as the latest C18 and C++17 standards. Special consideration is given to writing robust, reliable, and secure code that is portable across operating systems, including Winsock sockets for Windows and POSIX sockets for Linux and macOS. What you will learn * Uncover cross-platform socket programming APIs * Implement techniques for supporting IPv4 and IPv6 * Understand how TCP and UDP connections work over IP * Discover how hostname resolution and DNS work * Interface with web APIs using HTTP and HTTPS * Acquire hands-on experience with Simple Mail Transfer Protocol (SMTP) * Apply network programming to the Internet of Things (IoT) Who this book is for If you're a developer or a system administrator who wants to enter the world of network programming, this book is for you. Basic knowledge of C programming is assumed.

Gain basic skills in network forensics and learn how to apply them effectively Key Features * Investigate network threats with ease * Practice forensics tasks such as intrusion detection, network analysis, and scanning * Learn forensics investigation at the network level Book Description Network forensics is a subset of digital forensics that deals with network attacks and their investigation. In the era of network attacks and malware threat, it’s now more important than ever to have skills to investigate network attacks and vulnerabilities. Hands-On Network Forensics starts with the core concepts within network forensics, including coding, networking, forensics tools, and methodologies for forensic investigations. You’ll then explore the tools used for network forensics, followed by understanding how to apply those tools to a PCAP file and write the accompanying report. In addition to this, you will understand how statistical flow analysis, network enumeration, tunneling and encryption, and malware detection can be used to investigate your network. Towards the end of this book, you will discover how network correlation works and how to bring all the information from different types of network devices together. By the end of this book, you will have gained hands-on experience of performing forensics analysis tasks. What you will learn * Discover and interpret encrypted traffic * Learn about various protocols * Understand the malware language over wire * Gain insights into the most widely used malware * Correlate data collected from attacks * Develop tools and custom scripts for network forensics automation Who this book is for The book targets incident responders, network engineers, analysts, forensic engineers and network administrators who want to extend their knowledge from the surface to the deep levels of understanding the science behind network protocols, critical indicators in an incident and conducting a forensic search over the wire.

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key Features * Build machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlow * Learn model optimization, and understand how to scale your models using simple and secure APIs * Develop, train, tune and deploy neural network models to accelerate model performance in the cloud Book Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learn * Manage AI workflows by using AWS cloud to deploy services that feed smart data products * Use SageMaker services to create recommendation models * Scale model training and deployment using Apache Spark on EMR * Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker * Build deep learning models on AWS using TensorFlow and deploy them as services * Enhance your apps by combining Apache Spark and Amazon SageMaker Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Learn to distribute network traffic, optimize application delivery, and defend network-level threats. Key Features * Quickly get up and running with Azure networking solutions * Level-up your cloud networking skills by planning, implementing, configuring, and securing your infrastructure network with Azure * Leverage Azure networking services to provide applications highly available and fault tolerant environment Book Description Microsoft provides organizations with an effective way of managing their network with Azure's networking services. No matter the size of your organization, Azure provides a way to highly reliable performance and secure connectivity with its networking services. The book starts with an introduction to the Azure networking like creating Azure virtual networks, designing address spaces and subnets. Then you will learn to create and manage network security groups, application security groups, and IP addresses in Azure. Gradually, we move on to various aspects like S2S, P2S, and Vnet2Vnet connections, DNS and routing, load balancers and traffic manager. This book will cover every aspect and function required to deliver practical recipes to help readers learn from basic cloud networking practices to planning, implementing, and securing their infrastructure network with Azure. Readers will not only be able to upscale their current environment but will also learn to monitor, diagnose, and ensure secure connectivity. After learning to deliver a robust environment readers will also gain meaningful insights from recipes on best practices. By the end of this book, readers will gain hands-on experience in providing cost-effective solutions that benefit organizations. What you will learn * Learn to create Azure networking services * Understand how to create and work on hybrid connections * Configure and manage Azure network services * Learn ways to design high availability network solutions in Azure * Discover how to monitor and troubleshoot Azure network resources * Learn different methods of connecting local networks to Azure virtual networks Who this book is for This book is targeted towards cloud architects, cloud solution providers, or any stakeholders dealing with networking on the Azure cloud. Some prior understanding of Microsoft Azure will be a plus point.

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features * Train your own models for effective prediction, using high-level Keras API * Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks * Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha Book Description TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques. What you will learn * Use tf.Keras for fast prototyping, building, and training deep learning neural network models * Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files * Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications * Understand image recognition techniques using TensorFlow * Perform neural style transfer for image hybridization using a neural network * Code a recurrent neural network in TensorFlow to perform text-style generation Who this book is for Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features * Understand effective synchronization strategies for faster processing using GPUs * Write parallel processing scripts with PyCuda and PyOpenCL * Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Book Description GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly. What you will learn * Utilize Python libraries and frameworks for GPU acceleration * Set up a GPU-enabled programmable machine learning environment on your system with Anaconda * Deploy your machine learning system on cloud containers with illustrated examples * Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm. * Perform data mining tasks with machine learning models on GPUs * Extend your knowledge of GPU computing in scientific applications Who this book is for Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.

Explore supervised machine learning with R by studying popular real-world uses cases such as object detection in driverless cars, customer churn, and default prediction Key Features * Study supervised learning algorithms by using real-world datasets * Fine tune optimal parameters with hyperparameter optimization * Select the best algorithm using the model evaluation framework Book Description R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. What you will learn * Develop analytical thinking to precisely identify a business problem * Wrangle data with dplyr, tidyr, and reshape2 * Visualize data with ggplot2 * Validate your supervised machine learning model using k-fold * Optimize hyperparameters with grid and random search, and Bayesian optimization * Deploy your model on Amazon Web Services (AWS) Lambda with plumber * Improve your model’s performance with feature selection and dimensionality reduction Who this book is for This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book.

A definitive guide covering all the core concepts necessary to pass the Salesforce Administrator Certification exam Key Features * Understand critical design considerations for setting up Salesforce CRM * Facilitate and manage the transition from Salesforce Classic to Lightning Experience * Learn key functions of application security to manage user profiles and data Book Description Salesforce’s winter ’19 release offers a host of new features for CRM designed to meet your sales and marketing requirements. With this comprehensive guide to implementing Salesforce CRM, administrators of all levels can easily get a thorough understanding of the platform. This Salesforce handbook begins by guiding you in setting up users and security and then progresses to configuration, data management, and data analytics. You’ll discover process automation and approval mechanisms, while also exploring the functional areas of Sales Cloud, Service Cloud, Marketing Cloud, and Salesforce Chatter. This book covers Salesforce CRM system administration in a practical way, and it’ll serve as an invaluable reference for both new administrators and experienced professionals. Furthermore, you’ll also delve into Salesforce mobile apps and mobile administration, along with Salesforce Adoption Manager. You’ll gain insights into Lightning Experience, Salesforce's new app, and learn how its modern design and sleek interface helps you to build customizable components. Finally, we'll see how the two versions compare and help manage the transition from Salesforce Classic to Lightning Experience.?? By the end of the book, you will have mastered the techniques to configure and control various user interface features in Salesforce CRM. What you will learn * Configure a variety of user interface features in Salesforce CRM * Understand the capabilities of the Salesforce CRM sharing model * Explore Einstein Analytics - Salesforce's new wave of advanced reporting * Get to grips with the Lightning Process Builder workflow * Set up user profiles, security, and login access mechanisms * Find out how Apex and Visualforce coding can be used in Salesforce CRM * Manage the transition from Salesforce Classic to Lightning Experience * Implement data manipulation features to apply best practices in data management Who this book is for This book is for administrators who want to develop and strengthen their Salesforce CRM skills in the areas of configuration and system management. Whether you are a new or experienced professional, this book will enhance your knowledge and understanding of Salesforce CRM features.

All the key deep learning methods built step-by-step in PyTorch Key Features * Understand the internals and principles of PyTorch * Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more * Build deep learning workflows and take deep learning models from prototyping to production Book Description PyTorch is a new, lightweight, and Python-first tool for deep learning. Built by Facebook to offer flexibility and speed, it has quickly become the preferred tool for deep learning experts. PyTorch helps you release deep learning models faster than ever before. PyTorch Deep Learning Hands-On shows how to implement every major deep learning architecture in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools. Each chapter focuses on a different area of deep learning. Chapters start with a refresher on the core principles, before sharing the code you need to implement them in PyTorch. If you want to become a deep learning expert this book is for you. What you will learn Use PyTorch to build: * Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and more * Convolutional Neural Networks – create advanced computer vision systems * Recurrent Neural Networks – work with sequential data such as natural language and audio * Generative Adversarial Networks – create new content with models including SimpleGAN and CycleGAN * Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing * Deep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages * Production-ready models – package your models for high-performance production environments Who this book is for Machine learning professionals and enthusiasts who know Python and want to build efficient and powerful deep learning systems in PyTorch.

Achieve improved network programmability and automation by leveraging powerful network programming concepts, algorithms, and tools Key Features * Deal with remote network servers using SSH, FTP, SNMP and LDAP protocols. * Design multi threaded and event-driven architectures for asynchronous servers programming. * Leverage your Python programming skills to build powerful network applications Book Description Network programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be. Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python. As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks. What you will learn * Execute Python modules on networking tools * Automate tasks regarding the analysis and extraction of information from a network * Get to grips with asynchronous programming modules available in Python * Get to grips with IP address manipulation modules using Python programming * Understand the main frameworks available in Python that are focused on web application * Manipulate IP addresses and perform CIDR calculations Who this book is for If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. If you're a network engineer or a network professional aiming to be more productive and efficient in networking programmability and automation then this book would serve as a useful resource. Basic knowledge of Python is assumed.

Get up to speed with various penetration testing techniques and resolve security threats of varying complexity Key Features * Enhance your penetration testing skills to tackle security threats * Learn to gather information, find vulnerabilities, and exploit enterprise defenses * Navigate secured systems with the most up-to-date version of Kali Linux (2019.1) and Metasploit (5.0.0) Book Description Sending information via the internet is not entirely private, as evidenced by the rise in hacking, malware attacks, and security threats. With the help of this book, you'll learn crucial penetration testing techniques to help you evaluate enterprise defenses. You'll start by understanding each stage of pentesting and deploying target virtual machines, including Linux and Windows. Next, the book will guide you through performing intermediate penetration testing in a controlled environment. With the help of practical use cases, you'll also be able to implement your learning in real-world scenarios. By studying everything from setting up your lab, information gathering and password attacks, through to social engineering and post exploitation, you'll be able to successfully overcome security threats. The book will even help you leverage the best tools, such as Kali Linux, Metasploit, Burp Suite, and other open source pentesting tools to perform these techniques. Toward the later chapters, you'll focus on best practices to quickly resolve security threats. By the end of this book, you'll be well versed with various penetration testing techniques so as to be able to tackle security threats effectively What you will learn * Perform entry-level penetration tests by learning various concepts and techniques * Understand both common and not-so-common vulnerabilities from an attacker's perspective * Get familiar with intermediate attack methods that can be used in real-world scenarios * Understand how vulnerabilities are created by developers and how to fix some of them at source code level * Become well versed with basic tools for ethical hacking purposes * Exploit known vulnerable services with tools such as Metasploit Who this book is for If you’re just getting started with penetration testing and want to explore various security domains, this book is for you. Security professionals, network engineers, and amateur ethical hackers will also find this book useful. Prior knowledge of penetration testing and ethical hacking is not necessary.

Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key Features * Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms * Develop effective, robust, and fail-safe vision for your applications * Build computer vision algorithms with machine learning capabilities Book Description OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by setting up OpenCV, and explains how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of the book, you'll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects What you will learn * Install and create a program using the OpenCV library * Segment images into homogenous regions and extract meaningful objects * Apply image filters to enhance image content * Exploit image geometry to relay different views of a pictured scene * Calibrate the camera from different image observations * Detect people and objects in images using machine learning techniques * Reconstruct a 3D scene from images * Explore face detection using deep learning Who this book is for If you’re a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV.

Manipulate and analyze network data with the power of Python and NetworkX Key Features * Understand the terminology and basic concepts of network science * Leverage the power of Python and NetworkX to represent data as a network * Apply common techniques for working with network data of varying sizes Book Description NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems. What you will learn * Use Python and NetworkX to analyze the properties of individuals and relationships * Encode data in network nodes and edges using NetworkX * Manipulate, store, and summarize data in network nodes and edges * Visualize a network using circular, directed and shell layouts * Find out how simulating behavior on networks can give insights into real-world problems * Understand the ongoing impact of network science on society, and its ethical considerations Who this book is for If you are a programmer or data scientist who wants to manipulate and analyze network data in Python, this book is perfect for you. Although prior knowledge of network science is not necessary, some Python programming experience will help you understand the concepts covered in the book easily.

Gain a complete overview of Oracle CX Cloud Suite and its tools for functions ranging from marketing to sales and commerce to service Key Features * Make optimal use of your Oracle CX Cloud Suite to improve business results * Achieve improved customer insights through Oracle CX’s advanced capabilities * Learn how to design a CX solution architecture Book Description Oracle CX Cloud offers features and capabilities that help companies excel at sales, customer management, and much more. This book is a detailed guide to implementing cloud solutions and helping administrators of all levels thoroughly understand the platform. Oracle CX Cloud Suite begins with an introduction to high-level Oracle architecture and examines what CX offers over CRM. You’ll explore the different cloud-based tools for marketing, sales, and customer services, among others. The book then delves into deployment by covering basic settings, setting up users, and provisioning. You’ll see how to integrate the CX suite to work together to interact with the environment and connect with legacy systems, social connectors, and internet services. The book concludes with a use case demonstrating how the entire Oracle CX Suite is set up, and also covers how to leverage Oracle ICS and Oracle CX Cloud for hybrid deployment. By end of the book, you will have learned about the working of the Oracle CX Cloud Suite and how to orchestrate user experience across all products seamlessly. What you will learn * Differentiate between Oracle CRM and CX Cloud suites * Explore a variety of Oracle CX Cloud tools for marketing and sales * Set up users and database connections during deployment * Employ Cloud Suite CX tools to aid in planning and analysis * Implement hybrid Oracle CX solutions and connect with legacy systems * Integrate with social media connectors like Facebook and LinkedIn * Leverage Oracle ICS and Oracle CX Suite to improve business results Who this book is for This book is for administrators who want to develop and strengthen their Oracle CX Cloud Suite skills in the areas of configuration and system management. Whether you are a new administrator or an experienced professional, this book will enhance your understanding of the new Oracle CX features.

Discover techniques to enhance your videos with complex animation Key Features * Design, animate, and sequence a complete motion graphics project * Work with compositions and pre-compositions to create animations speedily * Maximize your motion graphics and visual effects skills Book Description If you’re thinking seriously about making and publishing your videos with professional editing and animation, look no further! Adobe After Effects is a popular tool among video editors and YouTubers to enhance their videos and bring them to life by implementing visual effects and motion graphics. This book will take you right from the basics through to the advanced techniques in Adobe After Effects CC 2018. You will start by setting up your editing environment to learn and improve techniques to sharpen your video editing skills. Furthermore, you will work with basic and advanced special effects to create, modify, and optimize motion graphics in your videos. Lastly, you will not only learn how to create 2.5D animations, but also get to grips with using Cinema 4D Lite to build and animate complete 3D scenes. By the end of the book, you’ll have learned how to package a video efficiently with the help of the projects covered. What you will learn * Create a lower third project for a TV show with complex layers * Work with shape layer animation to create an animated lyrics video * Explore different tools to animate characters * Apply text animation to create a dynamic film-opening title * Use professional visual effects to create a VFX project * Model, light, and composite your 3D project in After Effects Who this book is for If you’re a student, professional, YouTuber, or anyone who is interested in video editing, animation, and motion graphics, this book is for you. You’ll also find this resource useful if you’re an Illustrator looking to put your creations in motion.

Optimize your marketing strategies through analytics and machine learning Key Features * Understand how data science drives successful marketing campaigns * Use machine learning for better customer engagement, retention, and product recommendations * Extract insights from your data to optimize marketing strategies and increase profitability Book Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learn * Learn how to compute and visualize marketing KPIs in Python and R * Master what drives successful marketing campaigns with data science * Use machine learning to predict customer engagement and lifetime value * Make product recommendations that customers are most likely to buy * Learn how to use A/B testing for better marketing decision making * Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.

Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications Key Features * Explore advanced financial models used by the industry and ways of solving them using Python * Build state-of-the-art infrastructure for modeling, visualization, trading, and more * Empower your financial applications by applying machine learning and deep learning Book Description The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis. What you will learn * Solve linear and nonlinear models representing various financial problems * Perform principal component analysis on the DOW index and its components * Analyze, predict, and forecast stationary and non-stationary time series processes * Create an event-driven backtesting tool and measure your strategies * Build a high-frequency algorithmic trading platform with Python * Replicate the CBOT VIX index with SPX options for studying VIX-based strategies * Perform regression-based and classification-based machine learning tasks for prediction * Use TensorFlow and Keras in deep learning neural network architecture Who this book is for If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.