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.

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.

Solve real-world data problems with R and machine learning Key Features * Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond * Harness the power of R to build flexible, effective, and transparent machine learning models * Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn * Discover the origins of machine learning and how exactly a computer learns by example * Prepare your data for machine learning work with the R programming language * Classify important outcomes using nearest neighbor and Bayesian methods * Predict future events using decision trees, rules, and support vector machines * Forecast numeric data and estimate financial values using regression methods * Model complex processes with artificial neural networks — the basis of deep learning * Avoid bias in machine learning models * Evaluate your models and improve their performance * Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Make use of hands-on recipes for many tasks that are typically encountered in both the on-premises as well as the cloud world. Key Features * A recipe-based guide to help you build effective administrative solutions * Gain hands-on experience with the newly added features of PowerShell Core * Manage critical business environments with professional scripting practices Book Description This book will follow a recipe-based approach and start off with an introduction to the fundamentals of PowerShell, and explaining how to install and run it through simple examples. Next, you will learn how to use PowerShell to access and manipulate data and how to work with different streams as well. You will also explore the object model which will help with regard to PowerShell function deployment. Going forward, you will get familiar with the pipeline in its different use cases. The next set of chapters will deal with the different ways of accessing data in PowerShell. You will also learn to automate various tasks in Windows and Linux using PowerShell Core, as well as explore Windows Server. Later, you will be introduced to Remoting in PowerShell Core and Just Enough Administration concept. The last set of chapters will help you understand the management of a private and public cloud with PowerShell Core. You will also learn how to access web services and explore the high-performance scripting methods. By the end of this book, you will gain the skills to manage complex tasks effectively along with increasing the performance of your environment. What you will learn * Leverage cross-platform interaction with systems * Make use of the PowerShell recipes for frequent tasks * Get a better understanding of the inner workings of PowerShell * Understand the compatibility of built-in Windows modules with PowerShell Core * Learn best practices associated with PowerShell scripting * Avoid common pitfalls and mistakes Who this book is for This book will be for windows administrators who want to enhance their PowerShell scripting skills to the next level. System administrators wanting to automate common to complex tasks with PowerShell scripts would benefit from this book. Prior understanding on PowerShell would be necessary.

A problem-solution guide to encounter various NLP tasks utilizing Java open source libraries and cloud-based solutions Key Features * Perform simple-to-complex NLP text processing tasks using modern Java libraries Extract relationships between different text complexities using a problem-solution approach * Utilize cloud-based APIs to perform machine translation operations Book Description Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon’s AWS. You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentences, or semantic words. What you will learn * Explore how to use tokenizers in NLP processing * Implement NLP techniques in machine learning and deep learning applications * Identify sentences within the text and learn how to train specialized NER models * Learn how to classify documents and perform sentiment analysis * Find semantic similarities between text elements and extract text from a variety of sources * Preprocess text from a variety of data sources * Learn how to identify and translate languages Who this book is for This book is for data scientists, NLP engineers, and machine learning developers who want to perform their work on linguistic applications faster with the use of popular libraries on JVM machines. This book will help you build real-world NLP applications using a recipe-based approach. Prior knowledge of Natural Language Processing basics and Java programming is expected.

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.

A comprehensive guide to get started with Java and gain insights into major concepts such as object-oriented, functional, and reactive programming Key Features * Strengthen your knowledge of important programming concepts and the latest features in Java * Explore core programming topics including GUI programming, concurrency, and error handling * Learn the idioms and best practices for writing high-quality Java code Book Description Java is one of the preferred languages among developers, used in everything right from smartphones, and game consoles to even supercomputers, and its new features simply add to the richness of the language. This book on Java programming begins by helping you learn how to install the Java Development Kit. You will then focus on understanding object-oriented programming (OOP), with exclusive insights into concepts like abstraction, encapsulation, inheritance, and polymorphism, which will help you when programming for real-world apps. Next, you’ll cover fundamental programming structures of Java such as data structures and algorithms that will serve as the building blocks for your apps. You will also delve into core programming topics that will assist you with error handling, debugging, and testing your apps. As you progress, you’ll move on to advanced topics such as Java libraries, database management, and network programming, which will hone your skills in building professional-grade apps. Further on, you’ll understand how to create a graphic user interface using JavaFX and learn to build scalable apps by taking advantage of reactive and functional programming. By the end of this book, you’ll not only be well versed with Java 10, 11, and 12, but also gain a perspective into the future of this language and software development in general. What you will learn * Learn and apply object-oriented principles * Gain insights into data structures and understand how they are used in Java * Explore multithreaded, asynchronous, functional, and reactive programming * Add a user-friendly graphic interface to your application * Find out what streams are and how they can help in data processing * Discover the importance of microservices and use them to make your apps robust and scalable * Explore Java design patterns and best practices to solve everyday problems * Learn techniques and idioms for writing high-quality Java code Who this book is for Students, software developers, or anyone looking to learn new skills or even a language will find this book useful. Although this book is for beginners, professional programmers can benefit from it too. Previous knowledge of Java or any programming language is not required.

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features *A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ *A modern, practical and computational approach to Bayesian statistical modeling *A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn *Build probabilistic models using the Python library PyMC3 *Analyze probabilistic models with the help of ArviZ *Acquire the skills required to sanity check models and modify them if necessary *Understand the advantages and caveats of hierarchical models *Find out how different models can be used to answer different data analysis questions *Compare models and choose between alternative ones *Discover how different models are unified from a probabilistic perspective *Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

Leverage PowerShell to perform many day-to-day tasks in Microsoft Azure Key Features *Deploy and manage Azure virtual machines with PowerShell commands. *Get to grips with core concept of Azure PowerShell such as working with images and disks, custom script extension, high availability and more. *Leverage hands-on projects to successfully apply what you learned through the course of this book. Book Description As an IT professional, it is important to keep up with cloud technologies and learn to manage those technologies. PowerShell is a critical tool that must be learned in order to effectively and more easily manage many Azure resources. This book is designed to teach you to leverage PowerShell to enable you to perform many day-to-day tasks in Microsoft Azure. Taking you through the basic tasks of installing Azure PowerShell and connecting to Azure, you will learn to properly connect to an Azure tenant with PowerShell. Next, you will dive into tasks such as deploying virtual machines with PowerShell, resizing them, and managing their power states with PowerShell. Then, you will learn how to complete more complex Azure tasks with PowerShell, such as deploying virtual machines from custom images, creating images from existing virtual machines, and creating and managing of data disks. Later, you will learn how to snapshot virtual machines, how to encrypt virtual machines, and how to leverage load balancers to ensure high availability with PowerShell. By the end of this book, you will have developed dozens of PowerShell skills that are invaluable in the deployment and management of Azure virtual machines. What you will learn *Manage virtual machines with PowerShell *Resize a virtual machine with PowerShell *Create OS disk snapshots via PowerShell *Deploy new virtual machines from snapshots via PowerShell *Provision and attach data disks to a virtual machine via PowerShell *Load balance virtual machines with PowerShell *Manage virtual machines with custom script extensions Who this book is for This book is intended for IT professionals who are responsible for managing Azure virtual machines. No prior PowerShell or Azure experience is needed.

Learn development skills and improve productivity when programming in Microsoft Dynamics NAV 2018 - the popular Enterprise Resourse Planning management system used across a variety of industries for business process management Key Features *Solve common business problems with the valuable features and flexibility of Dynamics NAV *Understand the structure of NAV database - how documents and business entities are mapped to DB tables *Design user interface and bind the presentation layer with the data storage Book Description Microsoft Dynamics NAV is an enterprise resource planning (ERP) software suite for organizations. The system offers specialized functionality for manufacturing, distribution, government, retail, and other industries. This book gets you started with its integrated development environment for solving problems by customizing business processes. This book introduces the NAV development environment – C/SIDE. It gives an overview of the internal system language and the most essential development tools. The book will enable the reader to customize and extend NAV functionality with C/AL code, design a user interface through pages, create role centers, and build advanced reports in Microsoft Visual Studio. By the end of the book, you will have learned how to extend the NAV data model, how to write and debug custom code, and how to exchange data with external applications. What you will learn *Manage NAV Server configuration with Microsoft Management Console *Manage NAV installation with the NAV Administration Shell *Create integration events and extend functionality via the NAV event model *Run XML Ports from C/AL code *Design reports and write client code in RDLC expressions Who this book is for This book is for experienced NAV users who have an understanding of basic programming concepts. Familiarity with NAV development environment or its internal development language-C/AL is not expected.

Architect scalable, reliable, and maintainable applications for enterprises with Python Key Features *Explore various Python design patterns used for enterprise software development *Apply best practices for testing and performance optimization to build stable applications *Learn about different attacking strategies used on enterprise applications and how to avoid them Book Description Dynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you’ll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You’ll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques. By the end of the book, you will have become proficient at building efficient enterprise applications in Python. What you will learn *Understand the purpose of design patterns and their impact on application lifecycle *Build applications that can handle large amounts of data-intensive operations *Uncover advanced concurrency techniques and discover how to handle a large number of requests in production *Optimize frontends to improve the client-side experience of your application *Effective testing and performance profiling techniques to detect issues in applications early in the development cycle *Build applications with a focus on security *Implement large applications as microservices to improve scalability Who this book is for If you’re a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to understand the concepts covered in this book.

Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key Features *Bridge your data analysis with the power of programming, complex algorithms, and AI *Use Python and its extensive libraries to power your way to new levels of data insight *Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series *Explore this modern approach across with key industry case studies and hands-on projects Book Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learn *A new toolset that has been carefully crafted to meet for your data analysis challenges *Full and detailed case studies of the toolset across several of today’s key industry contexts *Become super productive with a new toolset across Python and Jupyter Notebook *Look into the future of data science and which directions to develop your skills next Who this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key Features * Get introduced to Python's rich suite of libraries for statistical modeling * Implement regression, clustering and train neural networks from scratch * Includes real-world examples on training end-to-end machine learning systems in Python Book Description Python's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learn * Understand the importance of statistical modeling * Learn about the various Python packages for statistical analysis * Implement algorithms such as Naive Bayes, random forests, and more * Build predictive models from scratch using Python's scikit-learn library * Implement regression analysis and clustering * Learn how to train a neural network in Python Who this book is for If you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

Design and implement professional-level programs by leveraging modern data structures and algorithms in Rust Key Features * Improve your productivity by writing more simple and easy code in Rust * Discover the functional and reactive implementations of traditional data structures * Delve into new domains of Rust, including WebAssembly, networking, and command-line tools Book Description Rust is a powerful language with a rare combination of safety, speed, and zero-cost abstractions. This Learning Path is filled with clear and simple explanations of its features along with real-world examples, demonstrating how you can build robust, scalable, and reliable programs. You’ll get started with an introduction to Rust data structures, algorithms, and essential language constructs. Next, you will understand how to store data using linked lists, arrays, stacks, and queues. You’ll also learn to implement sorting and searching algorithms, such as Brute Force algorithms, Greedy algorithms, Dynamic Programming, and Backtracking. As you progress, you’ll pick up on using Rust for systems programming, network programming, and the web. You’ll then move on to discover a variety of techniques, right from writing memory-safe code, to building idiomatic Rust libraries, and even advanced macros. By the end of this Learning Path, you’ll be able to implement Rust for enterprise projects, writing better tests and documentation, designing for performance, and creating idiomatic Rust code. This Learning Path includes content from the following Packt products: * Mastering Rust - Second Edition by Rahul Sharma and Vesa Kaihlavirta * Hands-On Data Structures and Algorithms with Rust by Claus Matzinger What you will learn * Design and implement complex data structures in Rust * Create and use well-tested and reusable components with Rust * Understand the basics of multithreaded programming and advanced algorithm design * Explore application profiling based on benchmarking and testing * Study and apply best practices and strategies in error handling * Create efficient web applications with the Actix-web framework * Use Diesel for type-safe database interactions in your web application Who this book is for If you are already familiar with an imperative language and now want to progress from being a beginner to an intermediate-level Rust programmer, this Learning Path is for you. Developers who are already familiar with Rust and want to delve deeper into the essential data structures and algorithms in Rust will also find this Learning Path useful.

Teach your machine to think for itself! Key Features * Delve into supervised learning and grasp how a machine learns from data * Implement popular machine learning algorithms from scratch, developing a deep understanding along the way * Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learn * Crack how a machine learns a concept and generalize its understanding to new data * Uncover the fundamental differences between parametric and non-parametric models * Implement and grok several well-known supervised learning algorithms from scratch * Work with models in domains such as ecommerce and marketing * Expand your expertise and use various algorithms such as regression, decision trees, and clustering * Build your own models capable of making predictions * Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are 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.

Delve into the world of Kotlin and learn to build powerful Android and web applications Key Features * Learn the fundamentals of Kotlin to write high-quality code * Test and debug your applications with the different unit testing frameworks in Kotlin * Explore Kotlin's interesting features such as null safety, reflection, and annotations Book Description Kotlin is a general-purpose programming language used for developing cross-platform applications. Complete with a comprehensive introduction and projects covering the full set of Kotlin programming features, this book will take you through the fundamentals of Kotlin and get you up to speed in no time. Learn Kotlin Programming covers the installation, tools, and how to write basic programs in Kotlin. You'll learn how to implement object-oriented programming in Kotlin and easily reuse your program or parts of it. The book explains DSL construction, serialization, null safety aspects, and type parameterization to help you build robust apps. You'll learn how to destructure expressions and write your own. You'll then get to grips with building scalable apps by exploring advanced topics such as testing, concurrency, microservices, coroutines, and Kotlin DSL builders. Furthermore, you'll be introduced to the kotlinx.serialization framework, which is used to persist objects in JSON, Protobuf, and other formats. By the end of this book, you'll be well versed with all the new features in Kotlin and will be able to build robust applications skillfully. What you will learn * Explore the latest Kotlin features in order to write structured and readable object-oriented code * Get to grips with using lambdas and higher-order functions * Write unit tests and integrate Kotlin with Java code * Create real-world apps in Kotlin in the microservices style * Use Kotlin extensions with the Java collections library * Uncover destructuring expressions and find out how to write your own * Understand how Java-nullable code can be integrated with Kotlin features Who this book is for If you’re a beginner or intermediate programmer who wants to learn Kotlin to build applications, this book is for you. You’ll also find this book useful if you’re a Java developer interested in switching to Kotlin.

Take your SAP ABAP skills to the next level by mastering ABAP programming techniques with the help of real-world examples Key Features * Become adept at building interfaces and explore ABAP tools and techniques * Discover the modern functionalities available in the latest version of ABAP * Learn the process of creating stunning HTML5 applications using SAPUI5 Book Description Advanced Business Application Programming (ABAP) is an established and complex programming language in the IT industry. This book is designed to help you use the latest ABAP techniques and apply legacy constructions using practical examples. You'll start with a quick refresher on language and database concepts, followed by agile techniques for adding custom code to a modern ABAP system. After this, you will get up to speed with the complete ABAP toolset for importing data to and from different environments. Next, you'll learn how to print forms and work with the different ABAP tools for Extensible Markup Language (XML) manipulation. While covering further chapters, you'll gain insights into building stunning UI5 interfaces, in addition to learning how to develop simple apps using the Business Object Processing Framework (BOPF). You will also pick up the technique of handling exceptions and performing testing in ABAP. In the concluding chapters, you can look forward to grasping various techniques for optimizing the performance of programs using a variety of performance analysis tools. By the end of this book, you will have the expertise you need to confidently build maintainable programs in Systems, Applications, and Products (SAP). What you will learn * Create stable and error-free ABAP programs * Leverage new ABAP concepts including object-oriented programming(OOP) and Model-View-Controller (MVC) * Learn to add custom code to your existing SAP program * Speed up your ABAP programs by spotting bottlenecks * Understand techniques such as performance tuning and optimization * Develop modern and beautiful user interfaces (UIs) in an ABAP environment * Build multiple classes with any nesting level Who this book is for This book is for developers who want to learn and use ABAP skills to become an industry expert. Familiarity with object-oriented programming concepts is expected.

Manage Microsoft Azure cloud services that span storage, security, networking, and compute cloud capabilities and ace the AZ-103 Exam Key Features * Master features and concepts pertaining to Azure's Administration services * Gain a deep understanding of various Azure services related to infrastructure, applications, and environments * Gauge yourself by giving mock tests with up-to-date exam questions Book Description Microsoft Azure Administrator – Exam Guide AZ-103 will cover all the exam objectives that will help you earn Microsoft Azure Administrator certification. Whether you want to clear AZ-103 exam or want hands-on experience in administering Azure, this study guide will help you achieve your objective. It covers the latest features and capabilities around configuring, managing, and securing Azure resources. Following Microsoft's AZ-103 exam syllabus, this guide is divided into five modules. The first module talks about how to manage Azure subscriptions and resources. You will be able to configure Azure subscription policies at Azure subscription level and learn how to use Azure policies for resource groups. Later, the book covers techniques related to implementing and managing storage in Azure. You will be able to create and configure backup policies and perform restore operations. The next module will guide you to create, configure, and deploy virtual machines for Windows and Linux. In the last two modules, you will learn about configuring and managing virtual networks and managing identities. The book concludes with effective mock tests along with answers so that you can confidently crack this exam. By the end of this book, you will acquire the skills needed to pass Exam AZ-103. What you will learn * Configure Azure subscription policies and manage resource groups * Monitor activity log by using Log Analytics * Modify and deploy Azure Resource Manager (ARM) templates * Protect your data with Azure Site Recovery * Learn how to manage identities in Azure * Monitor and troubleshoot virtual network connectivity * Manage Azure Active Directory Connect, password sync, and password writeback Who this book is for This book is for Azure administrators, systems administrators or anyone preparing for AZ 103 exam and wants to master Azure's various administration features. Readers should have proficiency in working with PowerShell, CLI and other day-to-day Azure administration tasks.

Build Prometheus ecosystems with metric-centric visualization, alerting, and querying Key Features * Integrate Prometheus with Alertmanager and Grafana for building a complete monitoring system * Explore PromQL, Prometheus' functional query language, with easy-to-follow examples * Learn how to deploy Prometheus components using Kubernetes and traditional instances Book Description Prometheus is an open source monitoring system. It provides a modern time series database, a robust query language, several metric visualization possibilities, and a reliable alerting solution for traditional and cloud-native infrastructure. This book covers the fundamental concepts of monitoring and explores Prometheus architecture, its data model, and how metric aggregation works. Multiple test environments are included to help explore different configuration scenarios, such as the use of various exporters and integrations. You’ll delve into PromQL, supported by several examples, and then apply that knowledge to alerting and recording rules, as well as how to test them. After that, alert routing with Alertmanager and creating visualizations with Grafana is thoroughly covered. In addition, this book covers several service discovery mechanisms and even provides an example of how to create your own. Finally, you’ll learn about Prometheus federation, cross-sharding aggregation, and also long-term storage with the help of Thanos. By the end of this book, you’ll be able to implement and scale Prometheus as a full monitoring system on-premises, in cloud environments, in standalone instances, or using container orchestration with Kubernetes. What you will learn * Grasp monitoring fundamentals and implement them using Prometheus * Discover how to extract metrics from common infrastructure services * Find out how to take full advantage of PromQL * Design a highly available, resilient, and scalable Prometheus stack * Explore the power of Kubernetes Prometheus Operator * Understand concepts such as federation and cross-shard aggregation * Unlock seamless global views and long-term retention in cloud-native apps with Thanos Who this book is for If you’re a software developer, cloud administrator, site reliability engineer, DevOps enthusiast or system admin looking to set up a fail-safe monitoring and alerting system for sustaining infrastructure security and performance, this book is for you. Basic networking and infrastructure monitoring knowledge will help you understand the concepts covered in this book.

Explore the aspects of financial modeling with the help of clear and easy-to-follow instructions and a variety of Excel features, functions, and productivity tips Key Features * A non data professionals guide to exploring Excel's financial functions and pivot tables * Learn to prepare various models for income and cash flow statements, and balance sheets * Learn to perform valuations and identify growth drivers with real-world case studies Book Description Financial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Microsoft Excel 2019 examines various definitions and relates them to the key features of financial modeling with the help of Excel. This book will help you understand financial modeling concepts using Excel, and provides you with an overview of the steps you should follow to build an integrated financial model. You will explore the design principles, functions, and techniques of building models in a practical manner. Starting with the key concepts of Excel, such as formulas and functions, you will learn about referencing frameworks and other advanced components of Excel for building financial models. Later chapters will help you understand your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. The book takes an intuitive approach to model testing, along with best practices and practical use cases. By the end of this book, you will have examined the data from various use cases, and you will have the skills you need to build financial models to extract the information required to make informed business decisions. What you will learn * Identify the growth drivers derived from processing historical data in Excel * Use discounted cash flow (DCF) for efficient investment analysis * Build a financial model by projecting balance sheets, profit, and loss * Apply a Monte Carlo simulation to derive key assumptions for your financial model * Prepare detailed asset and debt schedule models in Excel * Discover the latest and advanced features of Excel 2019 * Calculate profitability ratios using various profit parameters Who this book is for This book is for data professionals, analysts, traders, business owners, and students, who want to implement and develop a high in-demand skill of financial modeling in their finance, analysis, trading, and valuation work. This book will also help individuals that have and don't have any experience in data and stats, to get started with building financial models. The book assumes working knowledge with Excel.