9781394155378 - Applied Machine Learning for Data Science Practitioners de Subramanian, Vidya (17 resultados)

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Hardcover. Condición: new. Hardcover. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). "This book provides an excellent, practical compendium of the foundational topics in data science and machine learning, from a true expert. This book shows how Data Science and Machine Learning fit together in a workflow and learning that workflow is an essential foundation for building ML systems. I highly recommend this book for anyone who wants to master the fundamentals of building and analyzing ML models."Dr Anoop Sinha, Research Director, Google Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML.Data Preparation covers the process of framing ML problems and preparing data and features for modeling.ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection.Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model.ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics.Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardback. Condición: New. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML.Data Preparation covers the process of framing ML problems and preparing data and features for modeling.ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection.Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model.ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics.Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.

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Hardcover. Condición: Brand New. 656 pages. 11.02x11.02x2.36 inches. In Stock.

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Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Hardcover. Condición: new. Hardcover. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). "This book provides an excellent, practical compendium of the foundational topics in data science and machine learning, from a true expert. This book shows how Data Science and Machine Learning fit together in a workflow and learning that workflow is an essential foundation for building ML systems. I highly recommend this book for anyone who wants to master the fundamentals of building and analyzing ML models."Dr Anoop Sinha, Research Director, Google Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML.Data Preparation covers the process of framing ML problems and preparing data and features for modeling.ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection.Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model.ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics.Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardback. Condición: New. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML.Data Preparation covers the process of framing ML problems and preparing data and features for modeling.ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection.Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model.ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics.Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.

Hardcover. Condición: new. Hardcover. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). "This book provides an excellent, practical compendium of the foundational topics in data science and machine learning, from a true expert. This book shows how Data Science and Machine Learning fit together in a workflow and learning that workflow is an essential foundation for building ML systems. I highly recommend this book for anyone who wants to master the fundamentals of building and analyzing ML models."Dr Anoop Sinha, Research Director, Google Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML.Data Preparation covers the process of framing ML problems and preparing data and features for modeling.ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection.Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model.ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics.Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.