Título: Feature Engineering Bookcamp (English Edition)
Autor: Sinan Ozdemir
Sinopse: Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This books practical case-studies reveal feature engineering techniques that upgrade your data wranglingand your ML results. In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models to extract hidden patterns in data Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. Youll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your models performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead youll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more. About the technology Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline. About the book Feature Engineering Bookcamp walks you through six hands-on projects where youll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. Youll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomainsfrom natural language processing to time-series analysis. What's inside Identify and implement feature transformations Build machine learning pipelines with unstructured data Quantify and minimize bias in ML pipelines Use feature stores to build real-time feature engineering pipelines Enhance existing pipelines by manipulating input data About the reader For experienced machine learning engineers familiar with Python. About the author Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning. Table of Contents 1 Introduction to feature engineering 2 The basics of feature engineering 3 Healthcare: Diagnosing COVID-19 4 Bias and fairness: Modeling recidivism 5 Natural language processing: Classifying social media sentiment 6 Computer vision: Object recognition 7 Time series analysis: Day trading with machine learning 8 Feature stores 9 Putting it all together
Contexto da obra
Quando a classificação é mais ampla, o contexto do livro costuma depender ainda mais de autoria, tema e edição. “Feature Engineering Bookcamp (English Edition)”, de Sinan Ozdemir, publicado pela editora Manning, em 2022 e com 493 páginas, integra a categoria Livros Variados. Por isso, autoria, edição e tema acabam tendo ainda mais peso na forma de apresentar o livro.
Editora: Manning
Páginas: 493
Ano: 2022
Edição:
Linguagem: português
ISBN:
ISBN13:
Sobre a editora
Os livros da editora Manning apresentam uma experiência de leitura focada em tecnologia e programação, com ênfase em abordagens práticas e detalhadas. O catálogo traz obras que exploram desde frameworks específicos, como Spring e Grails, até paradigmas de programação como funcional e orientada a dados. Muitas obras combinam explicações conceituais com exemplos aplicados, facilitando a compreensão mesmo para leitores com conhecimentos variados, do iniciante ao avançado. O tom varia entre didático e técnico, com ritmo que privilegia a aplicação direta e o aprofundamento gradual. O material de apresentação indica uma preocupação em equilibrar teoria e prática, com textos que vão do tutorial ao estudo de casos reais.
