Wydawca: Packt Publishing
Arvind Ravulavaru
In this world of technology upgrades, IoT is currently leading with its promise to make the world a more smarter and efficient place.This book will show you how to build simple IoT solutions that will help you to understand how this technology works. We would not only explore the IoT solution stack, but we will also see how to do it with the world’s most misunderstood programming language - JavaScript. Using Raspberry Pi 3 and JavaScript (ES5/ES6) as the base to build all the projects, you will begin with learning about the fundamentals of IoT and then build a standard framework for developing all the applications covered in this book. You will then move on to build a weather station with temperature, humidity and moisture sensors and further integrate Alexa with it. Further, you will build a smart wearable for understanding the concept of fall detection. You will then extend it with the 'If This Then That' (IFTTT) rules engine to send an email on fall detection. Finally, you will be working with the Raspberry Pi 3 camera module and surveillance with a bit of facial detection using Amazon Rekognition platform.At the end of the book, you will not only be able to build standalone exciting IoT applications but also learn how you can extend your projects to another level.
Practical Linux Security Cookbook. Click here to enter text
Tajinder Kalsi
With the growing popularity of Linux, more and more administrators have started moving to the system to create networks or servers for any task. This also makes Linux the first choice for any attacker now. Due to the lack of information about security-related attacks, administrators now face issues in dealing with these attackers as quickly as possible. Learning about the different types of Linux security will help create a more secure Linux system.Whether you are new to Linux administration or experienced, this book will provide you with the skills to make systems more secure.With lots of step-by-step recipes, the book starts by introducing you to various threats to Linux systems. You then get to walk through customizing the Linux kernel and securing local files. Next you will move on to manage user authentication locally and remotely and also mitigate network attacks. Finally, you will learn to patch bash vulnerability and monitor system logs for security.With several screenshots in each example, the book will supply a great learning experience and help you create more secure Linux systems.
Tajinder Kalsi
Over the last few years, system security has gained a lot of momentum and software professionals are focusing heavily on it. Linux is often treated as a highly secure operating system. However, the reality is that Linux has its share of security ?aws, and these security ?aws allow attackers to get into your system and modify or even destroy your important data. But there’s no need to panic, since there are various mechanisms by which these ?aws can be removed, and this book will help you learn about different types of Linux security to create a more secure Linux system. With a step-by-step recipe approach, the book starts by introducing you to various threats to Linux systems. Then, this book will walk you through customizing the Linux kernel and securing local files. Next, you will move on to managing user authentication both locally and remotely and mitigating network attacks. Later, you will learn about application security and kernel vulnerabilities. You will also learn about patching Bash vulnerability, packet filtering, handling incidents, and monitoring system logs. Finally, you will learn about auditing using system services and performing vulnerability scanning on Linux.By the end of this book, you will be able to secure your Linux systems and create a robust environment.
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.
