Apress, 2018. — 362 p. — ISBN: 1484235630. Code files only! Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in...
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur. — 2nd Edition. — Packt Publishing, 2020. — 488 p. — ISBN: 978-1-80020-904-6. Code files only! Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms You already know you want to understand supervised learning, and a smarter way to do that is to...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 420 p. — ISBN: 978-1-119-64214-5. Code files only! Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques...
Packt Publishing, 2019. — 158 p. — ISBN: 978-1-83855-035-6. Code files only! This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Machine learning is an essential...
Packt Publishing, 2018. — 576 p. — ISBN: 978-1788621113. !Code files only Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms...
2nd Edition. — Packt Publishing, 2018. — 514 p. — ISBN: 978-1-78934-799-9. Code files only! Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Machine learning has gained tremendous popularity for its powerful...
Packt, 2020. — 256 p. — ISBN: 9781789801781. !Code files only Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular...
Apress, 2018. — 690 p. — ISBN: 9781484233573. !Code files Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so...
Packt Publishing, 2019. - 334p. - ISBN: 9781789807943 !Code files only Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. !Only code files Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine...
2nd Edition. — Packt Publishing, 2019. — 372 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
Packt Publishing, 2019. — 360 p. — ISBN: 978-1-83864-535-9. Code files only! This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. AI is changing the world – and with this book, anyone can start building intelligent software! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of...
Packt Publishing, 2017. — 438p. — ISBN: 978-1788294041. !Code files only An effective guide to using ensemble techniques to enhance machine learning models Key Features Learn how to maximize popular machine learning algorithms such as random forests, decision trees, AdaBoost, K-nearest neighbor, and more Get a practical approach to building efficient machine learning models...
Packt Publishing, 2020. — 348 p. — ISBN: 978-1-83882-441-9. Code files only! Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With...
O’Reilly, 2017. — 581 p. — ISBN: 9781491962299. Only sample files! Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete...
Birmingham: Packt Publishing, 2019. — 199 p. — ISBN: 1789345804. Python code files for the book ! Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to...
O’Reilly Media, Inc., 2020. — 366 p. — ISBN: 978-1-492-05319-4. Code files only! Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll...
Apress, 2017. — 204 p. - ISBN: 978-1-4842-2823-4 Code files only! Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business...
Leanpub, 2020. — 505 p. — ISBN NA. Code Files Only! This version was published on 2020-10-09 Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11. Important: I will not be updating this book to the new features introduced...
Manning, 2019. — 288 p. — ISBN: 1617295833. !Code files Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Machine Learning for Business teaches you how to make your company more automated, productive, and competitive by mastering practical, implementable machine learning techniques and tools. Thanks to the...
Packt Publishing, 2019. — 448 p. — ISBN: 978-1-78934-634-3. Code files only! Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the...
Packt, 2018. — 503 p. — ISBN: 178934641X. Code files only! The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book...
2nd Edition. — Aaron Jones, Christopher Kruger, Benjamin Johnston. — Packt Publishing Limited, July 2020. — 549 p. — ISBN: 978-1-80020-070-8. Code files only! With the help of interesting examples and activities, learn how to apply unsupervised algorithms on unlabelled datasets Do you find it difficult to learn how big companies like WhatsApp and Amazon find valuable insight...
Packt, 2018. - 356p. - ISBN: 9781788998246 Code files only! A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in...
Packt Publishing Ltd., 2020. — 530 p. — ISBN: 978-1-78995-533-0. Code files only! Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets C++ can make your machine learning models run faster and more efficiently. This handy guide...
Packt Publishing, 2018. — 2014 p. — ISBN: 1789138132. !Code files only START READING Book Description Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them...
3rd Edition. — Packt Publishing Ltd., 2019. — 458 p. — ISBN: 978-1-78829-586-4. Code files only! Solve real-world data problems with R and machine learning 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,...
3rd edition. — Packt Publishing, 2020. — 527 p. — ISBN 9781800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data scientists...
Apress, 2019. — 384 p. - ISBN: 978-1-4842-3787-8 Code files only! Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only...
Packt Publishing, 2019. — 293 p. — ISBN: 978-1-78934-979-5. Code files only! Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning...
Manning Publications, 2021. — 426 p. — ISBN 9781617296741. Code Files Only! Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the...
Manning Publications Co, 2021. — 493 p. — ISBN 9781617295645. Code Files Only! At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed...
Apress, 2019. — 347 p. — ISBN: 1484239156. 2nd.ed. (Code files) Harness the power of MatLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MatLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that...
New York: O’Reilly, 2019. — 220 p. — ISBN: 9781492039822. Code files only! Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical...
Packt Publishing, 2017. — 501 p. — ISBN: 978-1787125933. Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is...
Packt Publishing, 2019. — 252 p.— ISBN 1838828974, 9781838828974. Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Code files only! Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a...
Packt Publishing, 2019. — 285 p. — ISBN: 978-1-83882-173-9. Code files only! Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use...
Manning Publications Co., 2019. — 280 p. — ISBN: 978-1617293702. Code files only! MEAP Version 7 It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
Packt Publishing, 2020. — 490 p. — ISBN 9781800208919. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features Build, train, and deploy machine learning models quickly using Amazon SageMaker...
Packt, 2020. — 246 p. — ISBN: 9781838646486. Code files only! Build secure private blockchain networks to handle mission-critical security challenges such as denial-of-service attacks, user wallets, and pool mining attacks Key Features Explore blockchain concepts such as cryptography, consensus algorithms, and security assumptions Architect network security for mission-critical...
Packt Publishing Ltd., 2020. — 404 p. — ISBN: 978-1-78995-608-5. Code files only! Use the power of deep learning with Python to build and deploy intelligent web applications When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you’ll cover the latest tools and technological practices that are being used to implement deep...
Manning Publications, 2018. — 224 p. — ISBN: 978-1-6172-9333-7. Code files only! Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine...
Apress, 2017. - 358p. - ISBN: 978-1484228654 Code files only! Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning...
O’Reilly Media, 2020. — 432 p. — ISBN: 978-1-492-07305-5. Code files only! Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in...
Самиздат, 2022. — 106 с. Чему Вы научитесь: Строить дашборды и писать SQL запросы. Оценивать влияние моделей на показатели бизнеса с помощью A/B-тестов. Деплоить модели и создавать свои микросервисы для ML
М.: ДМК Пресс, 2018. — 252 с. Н2О - простая в использовании и открытая библиотека, которая поддерживает большое количество операционных систем и языков программирования, а также масштабируется для обработки больших данных. Эта книга научит вас использовать алгоритмы машинного обучения, реализованные в Н2О, с упором на наиболее важные для продуктивной работы аспекты. Рассмотрены...
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