Informatyka

Machine Learning Fundamentals. Use Python and scikit-learn to get up and running with the hottest developments in machine learning

Hyatt Saleh

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.

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

Machine Learning Projects for Mobile Applications. Build Android and iOS applications using TensorFlow Lite and Core ML

Karthikeyan NG

Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so.The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google’s ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN.By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.

Machine Learning Quick Reference. Quick and essential machine learning hacks for training smart data models

Rahul Kumar

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner.After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered.By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.

Machine Learning Security Principles. Keep data, networks, users, and applications safe from prying eyes

John Paul Mueller

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.

Machine Learning Solutions. Expert techniques to tackle complex machine learning problems using Python

Jalaj Thanaki

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job.You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions.In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.

Machine Learning with Apache Spark Quick Start Guide. Uncover patterns, derive actionable insights, and learn from big data using MLlib

Jillur Quddus

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

Machine Learning with Core ML. An iOS developer's guide to implementing machine learning in mobile apps

Joshua Newnham

Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps.Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this book, you’ll walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You’ll learn to implement Core ML for visual-based applications using the principles of transfer learning and neural networks. Having got to grips with the basics, you’ll discover a series of seven examples, each providing a new use-case that uncovers how machine learning can be applied along with the related concepts. By the end of the book, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs

Machine Learning With Go. Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

Joseph Langstaff Whitenack, Richard Townsend

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.

Machine Learning with Go Quick Start Guide. Hands-on techniques for building supervised and unsupervised machine learning workflows

Michael Bironneau, Toby Coleman

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.

Machine Learning with R Cookbook. Analyze data and build predictive models - Second Edition

AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.

Machine Learning with R. Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data - Fourth Edition

Brett Lantz

Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic.Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering.With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights.Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques.Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.

Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5

Iván Pastor Sanz

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.

Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning

Brett Lantz

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of big data and data science. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.Machine Learning with R is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.Machine Learning with R will provide you with the analytical tools you need to quickly gain insight from complex data.

Machine Learning with Spark. Develop intelligent, distributed machine learning systems - Second Edition

Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet...

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.

Machine Learning with Swift. Artificial Intelligence for iOS

Alexander Sosnovshchenko

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.

Machine Learning with TensorFlow 1.x. Second generation machine learning with Google's brainchild - TensorFlow 1.x

Quan Hua, Saif Ahmed, Shams Ul Azeem

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics

Rich Collier, Bahaaldine Azarmi

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.

Machines That Think. How Artificial Intelligence Works and What It Means for Us

Rheinwerk Publishing, Inc, Inga Strümke

Machines That Think explores the evolution of artificial intelligence (AI), from its roots in early theoretical frameworks to modern machine learning technologies. It begins by diving into the history of AI, featuring the foundational contributions of pioneers like Alan Turing and John von Neumann. The book then examines the attempts to make machines intelligent, covering symbolic AI, expert systems, and the rise of neural networks. With a focus on the technological advancements that shaped AI, this book provides readers with a deep understanding of how AI systems have evolved over time.The book continues by addressing the growing importance of data in AI systems. It explores how data shapes machine learning models and the inherent challenges faced by data scientists when gathering and processing data for AI applications. The impact of data on model accuracy and the ethical dilemmas surrounding its collection and usage are also discussed. In the final chapters, the book delves into the ethical and societal implications of AI, exploring issues such as privacy, accountability, and the future of AI in everyday life. It speculates on the future of artificial general intelligence (AGI) and superintelligence, contemplating the potential consequences of these technologies.

Magento : Beginner's Guide - Second Edition. As a non-techie you might be a bit daunted at the thought of taking on an e-commerce system as powerful as Magento. But don't be – our guide makes it all so accessible with its user-friendly, absolute-beginner approach

Robbert Ravensbergen, Sander Schoneville

Magento is one of the most powerful, flexible, and customizable e-commerce systems. It offers you the ability to create and manage a profitable online store. As your online store grows, you can be sure that this robust e-commerce system can handle your needs. However, getting started with Magento can be difficult without the right guidance.Magento Beginner's Guide - Second Edition provides guidance in the form of a step-by-step approach to building a simple, effective online store. The book covers the key features of Magento that will help you get your store up and running. It guides you through installation, configuration, populating your store with products, accepting payments, maintaining relationships with your customers, and fulfilling orders.Magento Beginner's Guide - Second Edition guides you through the installation, configuration, populating your store with products, accepting payments, maintaining relationships with your customers, and fulfilling orders.You will start with installing Magento on a hosting provider or on your local machine for testing purposes. From there, you will learn everything Magento has to offer, from working with products to adding additional functions.After installing Magento, you'll learn how to set up your first store and add multiple stores in the same environment. One of Magento's strengths, the product catalogue, will also be discussed thoroughly. From there we'll continue to set up payment and shipping methods. You'll learn to use themes to give your store the look and feel you need. As well as that, you'll start using extensions to expand Magento's functionality and you'll learn to process your first orders.You will learn to customize the default Magento storefront so that it becomes your personalized store and also about Magento's directory structure and find out where some of the elements of a store are customized. As you work your way through each chapter, your store will grow in scope and sophistication. By the time you finish this book, you should have a complete, working online store.

Magento 1.3 Sales Tactics Cookbook. Solve real-world Magento sales problems with a collection of simple but effective recipes

William Rice

Magento is a feature-rich, professional open source e-commerce solution that offers users complete flexibility and control over the look, content, and functionality of their online store. Although Magento provides users with the power to create dynamic e-commerce sites, it can be challenging to get beyond the basics and create sites that are tailored to your unique business needs.This book gives you a hands-on experience on Magento, helping you increase your revenue by implementing proven sales tactics on your Magento site.This book contains techniques to help you with each stage of selling to your customers: attracting visitors, driving them to your product pages, making the sale, increasing sales, engaging your customers, and more. It contains a powerful mix of techniques for increasing sales in your online storefront. Over thirty of the techniques are short and stand alone. A few are designed to be used together, such as the seven techniques in the chapter on creating an international storefront. Some techniques make creative use of Magento’s standard features. A few techniques combine Magento with other tools, such as social networking sites. All the techniques are explained in clear, step-by-step directions, with many screenshots. The book uses an example site for each technique, so that you can see exactly how it will affect your storefront. You can jump into the book at any point, or, work through it from beginning to end.

Magento 2 Beginners Guide. Discover what you need to know to build your own profitable online stores using the power of Magento 2!

Gabriel Guarino

Do you have a good product to sell but need your start-up to sell it to your potential customers the right way? Were you unhappy with what Magento 1 had to offer and are looking forward to trying out what Magento 2 provides? If either of these questions ring a bell, then this book is for you! You'll start by getting a general understanding of what Magento is, why and how you should use it, and whether it is possible and feasible to migrate from an old web store to Magento 2.We'll introduce you to the main e-commerce concepts and basic features and let you play with them, so you can get a taste of how catalog and content management works. Following on from that, we'll show you how to tune your store up. You will learn how to get web store offers up and running, how to offer various discounts in the catalog, how to let the customers reduce the total price in the shopping cart by combining different products, and how to generate coupon codes that customers can use. Finally, we'll get serious and turn your plaything into a real web store, teaching you how to run it for real.

Magento 2 Cookbook. Exploring Magento 2 in the form of recipes

Ray Bogman, Vladimir Kerkhoff

Magento 2 is an open source e-commerce platform that has all the functionality to function from small to large online stores. It is preferred by developers and merchants due to its new architecture, which makes it possible to extend the functionalities with plugins, a lot of which are now created by the community.This merchant and developer guide is packed with recipes that cover all aspects of Magento 2. The recipes start with simple how-to’s then delve into more advanced topics as the book progresses.We start with the basics of setting up a Magento 2 project on Apache or Nginx. Next, you will learn about basics including system tools and caching to get your Magento 2 system ready for the real work. We move on to simple tasks such as managing your store and catalog configuration. When you are familiar with this, we cover more complex features such as module and extension development. Then we will jump to the final part: advanced Magento 2 extensions. By the end of this book, you’ll be competent with all the development phases of Magento 2 and its most common elements.

Magento 2 Development Cookbook. Over 60 recipes that will tailor and customize your experience with Magento 2

Bart Delvaux

With the challenges of growing an online business, Magento 2 is an open source e-commerce platform with innumerable functionalities that gives you the freedom to make on-the-fly decisions. It allows you to customize multiple levels of security permissions and enhance the look and feel of your website, and thus gives you a personalized experience in promoting your business.