Publisher: Packt Publishing
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.
Andrich van Wyk
Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release.This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you’ll explore the intricacies of gradient boosting machines and LightGBM. You’ll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you’ll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI.By the end of this book, you’ll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.
Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning
Oliver Theobald
The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling.The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts.Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.
Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili,...
Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
Hannu Ranta
The ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions.You’ll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset.By the end of this book, you’ll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.
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. Expert techniques for predictive modeling - Third Edition
Brett Lantz
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Brett Lantz
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data.You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.
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.
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.
Md. Rezaul Karim
Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.
Kevin Jolly
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Rajdeep Dua, Manpreet Singh Ghotra
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, Oleksandr Baiev
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.
Saif Ahmed, Quan Hua, 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.
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.
Rich Collier, Camilla Montonen, Bahaaldine Azarmi
Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection.The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Roy Rubin, William Rice, 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.
Fernando J Miguel, Ray Bogman, Vladimir Kerkhoff,...
Magento is the leading e-commerce software trusted by world`s leading organizations. Used by thousands of merchants for their transactions worth billions, it provides the flexibility to customize the content and functionality of your website. Our Magento Course will help you gain knowledge and skills that are required to design & develop world class online stores.Magento 2 Development Essentials - This book begins by setting up Magento 2 before gradually moving onto setting the basic options of the Sell System. You will learn Search Engine Optimization aspects, create design and customize theme layout, and adjust the Magento System to achieve great performance.Magento 2 Cookbook – This book is divided into several recipes, which show you which steps to take to complete a specific action. It will cover configuring your categories and products, performance tuning, creating a theme, developing a module etc. At the end of this book, you will gain the knowledge to start building a success website.Mastering Magento 2 - This is a comprehensive guide to using the all new features and interface of Magento 2 to build, extend, and design online stores. This book is your roadmap to managing your Magento store which teaches advanced and successful techniques. Focusing on Magento's Community version, this book offers you advanced guidance on managing, optimizing, and extending your store while taking advantage of the new features of Magento 2.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• Magento 2 Development Essentials by Fernando J. Miguel • Magento 2 Cookbook by Ray Bogman and Vladimir Kerkhoff• Mastering Magento 2 by Bret Williams and Jonathan Bownds
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.
Branko Ajzele, Bartosz Górski
Magento is one of the most exciting, flexible, and customizable e-commerce systems. It offers you an extensive suite of powerful tools for creating and managing an online store. After years of development, Magento 2 introduces itself with a strong emphasis on modularity, Web API's, automated testing and overall new technology stack platform.The long-awaited Magento 2 release introduces a whole new e-commerce platform to develop online stores. The all new Magento 2 architecture, Web APIs, and a host of other features are equally challenging to master as much as they are exciting to use.Tshis book will ease the learning curve by offering step-by-step guidance on how to extend the core functionality of your Magento 2 store.This book is your one-stop guide to build and customize a quality e-commerce website from the latest version of one of the largest, fastest growing, and most popular e-commerce platforms—Magento 2.We start off with an introduction to the fundamental concepts of Magento to give you a foundation to work from. We then move on to configure the development and basic production environment for Magento. After this, you’ll get to grips with the major concepts and conventions that are new to the Magento 2 platform. We then delve deeper to get to the core of automated deployments, persisting data, writing data fixture scripts and applying various backend and frontend modifications. As we near the end of the book, you will learn to make API calls and write automated tests. Finally, you will be guided through building a full-blown helpdesk module from scratch.By the end of this book, you will have learned a wide range of techniques to extend and customize your Magento 2 store to fit the requirements of your business.
Nurul Ferdous, 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.