Publisher: Packt Publishing
Practical Machine Learning Cookbook. Supervised and unsupervised machine learning simplified
Atul Tripathi
Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Sunila Gollapudi
This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big dataThis is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application.With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.
Practical Machine Learning on Databricks. Seamlessly transition ML models and MLOps on Databricks
Debu Sinha
Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah...
With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way.Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.
Practical Memory Forensics. Jumpstart effective forensic analysis of volatile memory
Svetlana Ostrovskaya, Oleg Skulkin
Memory Forensics is a powerful analysis technique that can be used in different areas, from incident response to malware analysis. With memory forensics, you can not only gain key insights into the user's context but also look for unique traces of malware, in some cases, to piece together the puzzle of a sophisticated targeted attack.Starting with an introduction to memory forensics, this book will gradually take you through more modern concepts of hunting and investigating advanced malware using free tools and memory analysis frameworks. This book takes a practical approach and uses memory images from real incidents to help you gain a better understanding of the subject and develop the skills required to investigate and respond to malware-related incidents and complex targeted attacks. You'll cover Windows, Linux, and macOS internals and explore techniques and tools to detect, investigate, and hunt threats using memory forensics. Equipped with this knowledge, you'll be able to create and analyze memory dumps on your own, examine user activity, detect traces of fileless and memory-based malware, and reconstruct the actions taken by threat actors.By the end of this book, you'll be well-versed in memory forensics and have gained hands-on experience of using various tools associated with it.
Davide Bedin, Mark Russinovich
Over the last decade, there has been a huge shift from heavily coded monolithic applications to finer, self-contained microservices. Dapr is a new, open source project by Microsoft that provides proven techniques and best practices for developing modern applications. It offers platform-agnostic features for running your applications on public cloud, on-premises, and even on edge devices.This book will help you get to grips with microservice architectures and how to manage application complexities with Dapr in no time. You'll understand how Dapr offers ease of implementation while allowing you to work with multiple languages and platforms. You'll also understand how Dapr's runtime, services, building blocks, and software development kits (SDKs) help you to simplify the creation of resilient and portable microservices. Dapr provides an event-driven runtime that supports the essential features you need to build microservices, including service invocation, state management, and publish/subscribe messaging. You'll explore all of those in addition to various other advanced features with this practical guide to learning Dapr.By the end of this book, you'll be able to write microservices easily using your choice of language or framework by implementing industry best practices to solve problems related to distributed systems.
Davide Bedin, Mark Russinovich
This second edition will help you get to grips with microservice architectures and how to manage application complexities with Dapr in no time. You'll understand how Dapr simplifies development while allowing you to work with multiple languages and platforms. Following a C# sample, you'll understand how Dapr's runtime, building blocks, and software development kits (SDKs) help you to simplify the creation of resilient and portable microservices.Dapr provides an event-driven runtime that supports the essential features you need for building microservices, including service invocation, state management, and publish/subscribe messaging. You'll explore all of those in addition to various other advanced features with this practical guide to learning Dapr. With a focus on deploying the Dapr sample application to an Azure Kubernetes Service cluster and to the Azure Container Apps serverless platform, you’ll see how to expose the Dapr application with NGINX, YARP, and Azure API Management.By the end of this book, you'll be able to write microservices easily by implementing industry best practices to solve problems related to distributed systems.