Название: Ultimate Machine Learning with ML.NET: Build, Optimize, and Deploy Powerful Machine Learning Models for Data-Driven Insights with ML.NET, Azure Functions, and Web API
Автор: Kalicharan Mahasivabhattu, Deepti Bandi
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

“Empower Your .NET Journey with Machine Learning”. Dive into the world of Machine Learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it using essential tools like Model Builder and the command-line interface. Next, it dives into the heart of Machine Learning tasks using ML.NET, exploring classification, regression, and clustering with its versatile functionalities. It will delve deep into the process of selecting and fine-tuning algorithms to achieve optimal performance and accuracy. You will gain valuable insights into inspecting and interpreting ML.NET models, ensuring they meet your expectations and deliver reliable results. It will teach you efficient methods for saving, loading, and sharing your models across projects, facilitating seamless collaboration and reuse. ML.NET is particularly distinctive for its deep integration with the .NET ecosystem, enabling developers to leverage their existing expertise in C# and other .NET languages to build and deploy Machine Learning models seamlessly. Whether you are a seasoned software engineer or a budding data scientist, ML.NET opens the door to a world of opportunities in Machine Learning, providing a platform where creativity and problem-solving converge. ML.NET is known for its versatility, offering support for a wide range of Machine Learning tasks, including classification, regression, clustering, and recommendation systems. Its open-source nature, platform independence, and comprehensive documentation make it a preferred choice for many practitioners who seek a framework that is both powerful and easy to learn. Whether you are working on web applications, mobile apps, or desktop software, ML.NET is designed to be a part of your toolkit, making Machine Learning accessible and attainable for everyone.

Название: The Modern Business Data Analyst: A Case Study Introduction into Business Data Analytics with CRISP-DM and R
Автор: Dominik Jung
Издательство: Springer
Год: 2024
Страниц: 312
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB

This book illustrates and explains the key concepts of business data analytics from scratch, tackling the day-to-day challenges of a business data analyst. It provides you with all the professional tools you need to predict online shop sales, to conduct A/B tests on marketing campaigns, to generate automated reports with PowerPoint, to extract datasets from Wikipedia, and to create interactive analytics Web apps. Alongside these practical projects, this book provides hands-on coding exercises, case studies, the essential programming tools and the CRISP-DM framework which you'll need to kickstart your career in business data analytics. The different chapters prioritize practical understanding over mathematical theory, using realistic business data and challenges of the Junglivet Whisky Company to intuitively grasp key concepts and ideas. Designed for beginners and intermediates, this book guides you from business data analytics fundamentals to advanced techniques, covering a large number of different techniques and best-practices which you can immediately exploit in your daily work. The book does not assume that you have an academic degree or any experience with business data analytics or Data Science. All you need is an open mind, willingness to puzzle and think mathematically, and the willingness to write some R code. This book is your all-in-one resource to become proficient in business data analytics with R, equipped with practical skills for the real world.

Название: Artificial Intelligence in Forensic Science: An Emerging Technology in Criminal Investigation Systems
Автор: Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, Naveen Kumar
Издательство: CRC Press
Год: 2024
Страниц: 206
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Artificial Intelligence in Forensic Scienceaddresses the current and emerging opportunities being utilized to apply modern Artificial Intelligence (AI) technologies to current forensic and investigation practices. The book also showcases the increasing benefits of AI where and when it can be applied to various techniques and forensic disciplines. The increasing rate of sophisticated crimes has increased the opportunity and need for the forensic field to explore a variety of emerging technologies to counter criminals—and artificial intelligence is no exception. There are many current investigative challenges that, with ingenuity and application, can be helped with the application of AI, especially in the digital forensic and cyber-crime arena. The book also explains many practical studies that have been carried out to test AI technologies in crime detection, uncovering evidence, and identifying perpetrators. In the last decade, the use of AI is common in many fields and now is an ideal time to look at the various ways AI can be integrated into judicial, forensic, and criminal cases to better collect and analyze evidence, thereby improving outcomes. Artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA) are examples of Machine Learning approaches that play a significant role in providing unconventional solutions for fingerprint identification challenges. Making a feature vector and tutoring (teaching) the computer how to process it in accordance with predetermined criteria are the objectives of these techniques.

Название: Algorithmic Trading Systems and Strategies: A New Approach: Design, Build, and Maintain an Effective Strategy Search Mechanism
Автор: Viktoria Dolzhenko
Издательство: Apress
Год: 2024
Страниц: 452
Язык: английский
Формат: pdf (true), epub (true)
Размер: 12.1 MB

Design and develop a complex trading system from idea to operation. Old approaches were based on manually searching for strategy ideas. This book shows you how to create a system that will generate, optimize, and launch profitable strategies into a fully automatic mode. Imagine a system that only requires access to the Internet and electricity from a trader. This book describes the architecture and features of such a system and provides recommendations for further development. Most books cover only the implementation stage and overlook the design and maintenance of these systems. Here, you’ll walk through the entire process of creating a complex, scalable and easily supported system. For example, you’ll design an application based on microservice architecture and learn about development environments. You’ll also examine the advantages of horizontal scaling in the context of creating trading systems. Along the way, you’ll set up Kubernetes, connect the monitoring system, and understand the intricacies of continuous integration and continuous delivery. Testing and identifying only dozens of strategies is a thing of the past. Algorithmic Trading Systems and Strategies: A New Approach shows you how to analyze thousands in the same amount of time. The global trend to automate and algorithmize everything has now come to trading, and every year the number of algorithmic systems that help traders or that even trade independently is growing. Anyone can buy an algorithmic robot; you don’t need to be a programmer. Of course, you can create your own trading system using your own ideas. But what do you do if you have no programming skills? Genetic algorithms are a popular type of algorithm belonging to the population class. These are the ones I will use in the search for the optimal strategy. This class of algorithms was based on the ideas of Darwin’s theory of natural selection.

Название: Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks, 2nd Edition
Автор: Jalil Villalobos Alva
Издательство: Apress
Год: 2024
Страниц: 476
Язык: английский
Формат: pdf
Размер: 23.6 MB

Enhance your Data Science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You’ll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you’ll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. For data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language.

Название: Cloud-Native DevOps: Building Scalable and Reliable Applications
Автор: Mohammed Ilyas Ahmed
Издательство: Apress
Год: 2024
Страниц: 446
Язык: английский
Формат: pdf
Размер: 42.5 MB

This book delivers cloud-based applications that are both scalable and reliable, aligning with the evolving needs of the present user base. This innovative approach blends the agility of cloud computing with the efficiency of contemporary DevOps methodologies. The book starts by providing valuable insights into the ever-evolving software landscape of Cloud-native DevOps. It delves into strategies for crafting scalable and dependable applications and illustrates the art of leveraging open-source tools to enhance collaboration. It navigates through the integral role of microservices in contemporary software development and imparts the know-how to integrate them with DevOps practices. The book simplifies container technology, with a focus on Docker and Kubernetes. It further elucidates the process of streamlining software delivery through Continuous Integration and Continuous Deployment (CI/CD). As you progress, you will explore open-source tools, including Argo, Kubeflow Pipelines, and AWS Step Functions. The concluding section covers real-world case studies and best practices for implementing Cloud-native DevOps. The book reviews the core concepts covered throughout and provides an exciting glimpse into the future of Cloud-native DevOps. Upon completing this book, you will be well-versed in the principles and practices of Cloud-native DevOps, understand real-world applications, and recognize the critical importance of security in cloud computing. For DevOps engineers, cloud architects, software developers, system administrators, IT managers, team leads, and business decision makers.

Название: Tech Startup Toolkit: How to launch strong and exit big
Автор: Jothy Rosenberg
Издательство: Manning Publications
Год: 2024
Страниц: 313
Язык: английский
Формат: pdf (true)
Размер: 12.4 MB

Essential advice for anyone aspiring to start up a technology company, based on decades of business experience. In Tech Startup Toolkit, you'll discover the good, the bad, and the ugly of succeeding with a tech startup. Author Jothy Rosenberg reveals the insights he's learned from an entrepreneurial career that's seen both $100 million sales, dramatic failures, and everything in between. In Tech Startup Toolkit Jothy tells stories from his incredible career that will give guidance and inspiration to anyone who's ever thought of creating or running a company. Every personal story teaches a vital lesson for any would-be startup founder, ensuring you avoid the pitfalls that end less-prepared companies. Tech Startup Toolkit covers everything a new founder needs to ensure a great idea can become a stable tech company that's ripe for acquisition. In 31 short anecdotes from Jothy's extensive experience, you'll learn how to pitch investors, develop a go-to-market strategy, and build the leadership skills that really matter for a great startup CEO.

Название: Grokking Data Structures (Final)
Автор: Marcello La Rocca
Издательство: Manning Publications
Год: 2024
Страниц: 282
Язык: английский
Формат: pdf (true)
Размер: 16.8 MB

Don't be perplexed by data structures! This fun, friendly, and fully illustrated guide makes it easy to learn useful data structures you'll put to work every day. Grokking Data Structures makes it a breeze to learn the most useful day-to-day data structures. You'll follow a steady learning path from absolute basics to advanced concepts, all illustrated with fun examples, engaging industry stories, and hundreds of graphics and cartoons. This book teaches data structures to anyone, no matter what their background. You don’t need advanced math, you don’t need to have taken CS101 or any course; you don’t need to be a coding ninja, but some knowledge of Python might help. This book, like every book in the Grooking series, gives you an understanding of how things work: what data structure is, what the basic structures you can use are, and how can you objectively decide which data structure is better for your task. It’s a book for beginners, meaning that it assumes no previous knowledge and it leverages your intuition and visual memory, but even if you are familiar with the subject, you might find it useful to dust off and even get a deeper understanding of some of the topics. For readers who know the basics of Python. A perfect companion to Grokking Algorithms!

Название: Coding For Beginners - 19th Edition, 2024
Автор: Papercut Limited
Издательство: Papercut Limited
Год: 2024
Язык: английский
Формат: pdf
Размер: 44.6 MB

Если вы новичок и хотите изучить все, что вам нужно для начала работы с программированием, то "Программирование для начинающих"- это первое и единственное руководство. Это независимое руководство представляет собой пошаговое руководство с подробными объяснениями. Напишите полезные предложения с помощью этого нового руководства, которое написано на понятном английском языке. Узнайте все, что вам нужно знать о создании своего собственного потрясающего приложения. В этом неофициальном руководстве проблемы никогда не будут неразрешимыми, а ответы на вопросы будут даны по мере того, как вы будете изучать и совершенствовать свои навыки программирования.

Название: Diving Into Java Ocean: Handbook for Java beginners. Deep dive into the Java Ocean; don't just wet your toe
Автор: Ana Peterlić
Издательство: Independently published
Год: 2024
Страниц: 326
Язык: английский
Формат: epub
Размер: 14.7 MB

This is an introductory book on Java programming language, specially designed for Java beginners. It's a practical guide to Java ecosystem and contains everything you need to know to start programming in Java. With this book, you will learn: - Java basics: This book explains the basics of Java and teaches about classes, data types, structures, and algorithms. It is up to date for Java SE 22! - Basic OOP concepts and principles; - Lamda expressions, inner classes, working with files, and much more. There is no point in me telling you how great this book is - begin your journey and see for yourself! Let’s look at some core features that make Java the perfect choice when developing applications. First and foremost, Java is an object-oriented programming language. It emphasizes using classes and objects. Thanks to concepts such as inheritance, encapsulation, polymorphism, and abstraction, it encourages code reusability. You may have heard the saying, “Write once, run everywhere.”. This means that Java was designed to run independently of the operating system it’s running on. Platform independence is possible thanks to the JVM. Our code is translated into bytecode, which can run on any system with a compatible JVM. As a result, it simplifies cross-platform development. Unlike C programming language, Java doesn’t use pointers. Regarding objects, Java becomes very protective (like any other parent would) and doesn’t allow us anywhere near them. In other words, we can’t access objects directly or get the memory's address. The only way to access them is by using references.