Learning Concurrent Programming in Scala. Dive into the Scala framework with this programming guide, created to help you learn Scala and to build intricate, modern, scalable concurrent applications

Aleksandar Prokopec

Pentesting APIs. A practical guide to discovering, fingerprinting, and exploiting APIs

Maurício Harley

Understanding API security is crucial as APIs form the backbone of modern interconnected applications, making them prime targets for cyberattacks. Drawing on nearly 30 years of cybersecurity experience and an extensive background in network security and forensic analysis, this book provides the knowledge and tools to strengthen your API security practices and protect against cyber threats comprehensively.This book begins by establishing a foundational understanding of APIs, particularly focusing on REST and GraphQL, emphasizing their critical role and potential security vulnerabilities. It guides you through setting up a penetration testing environment to ensure the practical application of concepts. You’ll learn reconnaissance techniques, information-gathering strategies, and the discovery of API vulnerabilities. Authentication and authorization testing are thoroughly explored, covering mechanisms, weaknesses, and methods to bypass security controls. By comprehensively addressing these aspects, the book equips you to understand, identify, and mitigate risks, strengthening API security and effectively minimizing potential attack surfaces.By the end of this book, you’ll have developed practical skills to identify, exploit, and secure APIs against various vulnerabilities and attacks.

Dart By Example

David Mitchell

Python: Real World Machine Learning. Take your Python Machine learning skills to the next level

Prateek Joshi, Luca Massaron, John Hearty, Alberto Boschetti, ...

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you’ll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Python Machine Learning Cookbook by Prateek Joshi? Advanced Machine Learning with Python by John Hearty? Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

Python Tools for Visual Studio. Leverage the power of the Visual Studio IDE to develop better and more efficient Python projects

Martino A Sabia, Jia Shuan Cathy Wang

Blender 3D Asset Creation for the Metaverse. Unlock endless possibilities with 3D object creation, including metaverse characters and avatar models

Vinicius Machado Venâncio

If you are familiar with modeling, this book will help you discover a practical and efficient workflow designed to accelerate your asset creation process for use in multiple projects, including games and the metaverse.Complete with shortcuts and tips on how to speed up the process, this book guides you in modeling assets and characters with the help of references. You’ll learn how to optimize the modeled asset for maximum rendering performance within game engines and the metaverse. Next, you’ll get to grips with unwrapping the 3D model for texturing and explore multiple texturing techniques to breathe life into your 3D models. Finally, you’ll integrate the 3D model to work seamlessly across a myriad of programs and game engines.By the end of this book, you’ll have the skills to efficiently create any type of 3D asset from scratch for use in renders, animations, or immersive gaming experiences.

Hands-On Artificial Intelligence for Banking. A practical guide to building intelligent financial applications using machine learning techniques

Jeffrey Ng, Subhash Shah

Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI.You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI.

Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras

George Kyriakides, Konstantinos G. Margaritis

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.