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:
"Sinopsis" puede pertenecer a otra edición de este libro.
Vidya Subramanian is a passionate Data Science and Analytics leader, with experience leading teams at Google, Apple, and Intuit. Forbes recognized her as one of the "8 Female Analytics Experts From The Fortune 500." She authored Adobe Analytics with SiteCatalyst (Adobe Press) and McGraw-Hill's PMP Certification Mathematics (McGraw Hill). Vidya holds Master's degrees from Virginia Tech and Somaiya Institute of Management (India) and currently leads Data Science and Analytics for Google Play.
Applied Machine Learning for Data Science Practitioners
Vidya Subramanian
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:
"Sobre este título" puede pertenecer a otra edición de este libro.
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