Sztuczna inteligencja
Oliver Theobald
Starting with Python syntax and data types, this guide builds toward implementing key machine learning models. Learn about loops, functions, OOP, and data cleaning, then transition into algorithms like regression, KNN, and neural networks. A final section walks you through model optimization and building projects in Python.The book is split into two major sections—foundational Python programming and introductory machine learning. Readers are guided through essential concepts such as data types, variables, control flow, object-oriented programming, and using libraries like pandas for data manipulation.In the machine learning section, topics like model selection, supervised vs unsupervised learning, bias-variance, and common algorithms are demystified with practical coding examples. It’s a structured, clear roadmap to mastering both programming and applied ML from zero knowledge.
Oliver Theobald
This book is an ideal starting point for anyone interested in Artificial Intelligence and Machine Learning. It begins with the foundational principles of AI, offering a deep dive into its history, building blocks, and the stages of development. Readers will explore key AI concepts and gradually transition to practical applications, starting with machine learning algorithms such as linear regression and k-nearest neighbors. Through step-by-step Python tutorials, the book helps readers build and implement models with hands-on experience.As the book progresses, readers will dive into advanced AI topics like deep learning, natural language processing (NLP), and generative AI. Topics such as recommender systems and computer vision demonstrate the real-world applications of AI technologies. Ethical considerations and privacy concerns are also addressed, providing insight into the societal impact of these technologies.By the end of the book, readers will have a solid understanding of both the theory and practice of AI and Machine Learning. The final chapters provide resources for continued learning, ensuring that readers can continue to grow their AI expertise beyond the book.
Boštjan Kaluža, Krishna Choppella, Uday Kamath
Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books:[*]Java for Data Science[*]Machine Learning in Java [*]Mastering Java Machine LearningOn completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.
Joos Korstanje
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
Machine learning i natural language processing w programowaniu. Podręcznik z ćwiczeniami w Pythonie
Piotr Wróblewski
Wejdź na nowy poziom programowania z ML i NLP Sztuczna inteligencja stale się rozwija. Właściwie codziennie słyszymy o jej rosnących możliwościach, nowych osiągnięciach i przyszłości, jaką nam przyniesie. Jednak w tej książce skupiamy się nie na przyszłości, a na teraźniejszości i praktycznym obliczu AI - na usługach, które świadczy już dziś. Większość najciekawszych zastosowań sztucznej inteligencji bazuje na ML (uczenie maszynowe, ang. machine learning), NLP (przetwarzanie języka naturalnego, ang. natural language processing) i architekturze RAG (ang. retrieval augmented generation) zwiększającej możliwości tzw. dużych modeli językowych (LLM, ang. large language model). Stanowią one podwaliny budowy systemów AI, bez których te systemy często wcale nie mogłyby powstać. Do niedawna ML i NLP pozostawały domeną badaczy i specjalistów - znajdowały się poza zasięgiem praktyków programowania. Aktualnie jest inaczej, szybkie komputery, pojemne pamięci RAM i zaawansowane procesory pozwalają stosować te technologie w codziennej pracy programisty. Szczególnie programisty języka Python, do którego są one niemal "naturalnie" przypisane. Mało tego, od kodujących w Pythonie coraz częściej wręcz wymaga się umiejętności znajomości obszaru AI. Tym bardziej warto sięgnąć po ten podręcznik z ćwiczeniami, dzięki któremu między innymi: Dowiesz się, jak używać Pythona do rozwiązywania problemów AI Poznasz tajniki analizy tekstów, analizy sentymentu Zrozumiesz, jak skutecznie używać algorytmów klasyfikacji, regresji i grupowania do rozwiązywania problemów biznesowych Pokonwersujesz z ChatGPT - i to bez wchodzenia na stronę internetową tego serwisu
John Paul Mueller, Rod Stephens
Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references.The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies.This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.
Ashish Kumar, Shashank Kumar, Abbas Kudrati, Sarah...
With the rapid pace of digital change today, especially since the pandemic sped up digital transformation and technologies, it has become more important than ever to be aware of the unknown risks and the landscape of digital threats. This book highlights various risks and shows how business-as-usual operations carried out by unaware or targeted workers can lead your organization to a regulatory or business risk, which can impact your organization’s reputation and balance sheet.This book is your guide to identifying the topmost risks relevant to your business with a clear roadmap of when to start the risk mitigation process and what your next steps should be. With a focus on the new and emerging risks that remote-working companies are experiencing across diverse industries, you’ll learn how to manage risks by taking advantage of zero trust network architecture and the steps to be taken when smart devices are compromised. Toward the end, you’ll explore various types of AI-powered machines and be ready to make your business future-proof.In a nutshell, this book will direct you on how to identify and mitigate risks that the ever- advancing digital technology has unleashed.
Paul Battisson, Mike Wheeler
As applications built on the Salesforce platform are now a key part of many organizations, developers are shifting focus to Apex, Salesforce’s proprietary programming language. As a Salesforce developer, it is important to understand the range of tools at your disposal, how and when to use them, and best practices for working with Apex. Mastering Apex Programming will help you explore the advanced features of Apex programming and guide you in delivering robust solutions that scale.This book starts by taking you through common Apex mistakes, debugging, exception handling, and testing. You'll then discover different asynchronous Apex programming options and develop custom Apex REST web services. The book shows you how to define and utilize Batch Apex, Queueable Apex, and Scheduled Apex using common scenarios before teaching you how to define, publish, and consume platform events and RESTful endpoints with Apex. Finally, you'll learn how to profile and improve the performance of your Apex application, including architecture trade-offs.With code examples used to facilitate discussion throughout, by the end of the book, you'll have developed the skills needed to build robust and scalable applications in Apex.