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Podcasts
Machine Learning for Finance. Principles and practice for financial insiders
Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways.The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling
Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.
Tkinter is the built-in GUI package that comes with standard Python distributions. It is a cross-platform package, which means you build once and deploy everywhere. It is simple to use and intuitive in nature, making it suitable for programmers and non-programmers alike. This book will help you master the art of GUI programming. It delivers the bigger picture of GUI programming by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, media player, drawing application, chat application, screen saver, port scanner, and many more. In every project, you will build on the skills acquired in the previous project and gain more expertise. You will learn to write multithreaded programs, network programs, database driven programs and more. You will also get to know the modern best practices involved in writing GUI apps. With its rich source of sample code, you can build upon the knowledge gained with this book and use it in your own projects in the discipline of your choice.
Learn Computer Forensics. A beginner's guide to searching, analyzing, and securing digital evidence
A computer forensics investigator must possess a variety of skills, including the ability to answer legal questions, gather and document evidence, and prepare for an investigation. This book will help you get up and running with using digital forensic tools and techniques to investigate cybercrimes successfully. Starting with an overview of forensics and all the open source and commercial tools needed to get the job done, you'll learn core forensic practices for searching databases and analyzing data over networks, personal devices, and web applications. You'll then learn how to acquire valuable information from different places, such as filesystems, e-mails, browser histories, and search queries, and capture data remotely. As you advance, this book will guide you through implementing forensic techniques on multiple platforms, such as Windows, Linux, and macOS, to demonstrate how to recover valuable information as evidence. Finally, you'll get to grips with presenting your findings efficiently in judicial or administrative proceedings.By the end of this book, you'll have developed a clear understanding of how to acquire, analyze, and present digital evidence like a proficient computer forensics investigator.
Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, ...
Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before?With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises.The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Whether you’re looking to crack passwords as part of a thorough security audit or aiming to recover vital information, this book will equip you with the skills to accomplish your goals. Written by a cybersecurity expert with over fifteen years of experience in penetration testing, Ethical Password Cracking offers a thorough understanding of password protection and the correct approach to retrieving password-protected data.As you progress through the chapters, you first familiarize yourself with how credentials are stored, delving briefly into the math behind password cracking. Then, the book will take you through various tools and techniques to help you recover desired passwords before focusing on common cracking use cases, hash recovery, and cracking. Real-life examples will prompt you to explore brute-force versus dictionary-based approaches and teach you how to apply them to various types of credential storage.By the end of this book, you'll understand how passwords are protected and how to crack the most common credential types with ease.
Think about how much time you usually spend building an app in a technology you're still mastering—grasping new concepts, navigating roadblocks, and even rewriting entire modules as you learn. This book saves you that time, helping you create a modern .NET MAUI application like a pro.The chapters address a wide range of tasks and concepts essential for real-world apps, including UI best practices and advanced tips, MVVM, dependency injection, performance, and memory profiling. Since real-world applications often go beyond frontend development, this book also explores integration with backend services for authentication, data processing, synchronization, and real-time updates. Additionally, you’ll learn to implement multiple AI integration strategies, all without any prior machine learning experience.Mastery comes with practice, so the book is organized with step-by-step recipes, each tackling a specific task. Each recipe includes detailed explanations to help you apply what you're learning to your own unique projects.By the end of this book, you'll have developed the skills to build high-performance, interactive cross-platform applications with .NET MAUI, saving valuable time on your future projects.
The super-fast evolution of the JDK between versions 12 and 21 has made the learning curve of modern Java steeper, and increased the time needed to learn it. This book will make your learning journey quicker and increase your willingness to try Java’s new features by explaining the correct practices and decisions related to complexity, performance, readability, and more.Java Coding Problems takes you through Java’s latest features but doesn’t always advocate the use of new solutions — instead, it focuses on revealing the trade-offs involved in deciding what the best solution is for a certain problem.There are more than two hundred brand new and carefully selected problems in this second edition, chosen to highlight and cover the core everyday challenges of a Java programmer.Apart from providing a comprehensive compendium of problem solutions based on real-world examples, this book will also give you the confidence to answer questions relating to matching particular streams and methods to various problems.By the end of this book you will have gained a strong understanding of Java’s new features and have the confidence to develop and choose the right solutions to your problems.