Alexey grigorev (65 risultati)

Paperback. Condizione: Good. The only way to learn is to practice! In   Machine Learning Bookcamp, you?ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you?ve learned in previous chapters. By the end of the bookcamp, you?ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technologyMachine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that?s exactly what you?ll be doing in   Machine Learning Bookcamp. about the bookIn   Machine Learning Bookcamp  you?ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you?ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You?ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you?re done working through these fun and informative projects, you?ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.   what's inside Code fundamental ML algorithms from scratchCollect and clean data for training modelsUse popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlowApply ML to complex datasets with images and textDeploy ML models to a production-ready environment about the readerFor readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev  has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.…

Paperback. Condizione: Very Good. The only way to learn is to practice! In   Machine Learning Bookcamp, you?ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you?ve learned in previous chapters. By the end of the bookcamp, you?ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technologyMachine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that?s exactly what you?ll be doing in   Machine Learning Bookcamp. about the bookIn   Machine Learning Bookcamp  you?ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you?ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You?ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you?re done working through these fun and informative projects, you?ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.   what's inside Code fundamental ML algorithms from scratchCollect and clean data for training modelsUse popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlowApply ML to complex datasets with images and textDeploy ML models to a production-ready environment about the readerFor readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev  has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.…

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Paperback. Condizione: New. The only way to learn is to practice! In   Machine Learning Bookcamp, you'll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you've learned in previous chapters. By the end of the bookcamp, you'll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technologyMachine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that's exactly what you'll be doing in   Machine Learning Bookcamp. about the bookIn   Machine Learning Bookcamp  you'll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you'll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You'll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you're done working through these fun and informative projects, you'll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.   what's inside Code fundamental ML algorithms from scratchCollect and clean data for training modelsUse popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlowApply ML to complex datasets with images and textDeploy ML models to a production-ready environment about the readerFor readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev  has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.…

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Digital. Condizione: New. Use Java to create a diverse range of Data Science applications and bring Data Science into productionAbout This Book. An overview of modern Data Science and Machine Learning libraries available in Java. Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine.Who This Book Is ForThis book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it.If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you!What You Will Learn. Get a solid understanding of the data processing toolbox available in Java. Explore the data science ecosystem available in Java. Find out how to approach different machine learning problems with Java. Process unstructured information such as natural language text or images. Create your own search engine. Get state-of-the-art performance with XGBoost. Learn how to build deep neural networks with DeepLearning4j. Build applications that scale and process large amounts of data. Deploy data science models to production and evaluate their performanceIn DetailJava is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises.Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort.This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data.Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.Style and approachThis is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.…

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