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Journey to become a Google Cloud machine learning engineer : build the mind and hand of a Google certified ML professional /
Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills. This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on sk...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd.,
2022.
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Song, Logan, |e author. | |
| 245 | 1 | 0 | |a Journey to become a Google Cloud machine learning engineer : |b build the mind and hand of a Google certified ML professional / |c Dr. Logan Song. |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing Ltd., |c 2022. | |
| 300 | |a 1 online resource (330 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills. This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Starting with GCP and Python -- Chapter 1: Comprehending Google Cloud Services -- Understanding the GCP global infrastructure -- Getting started with GCP -- Creating a free-tier GCP account -- Provisioning our first computer in Google Cloud -- Provisioning our first storage in Google Cloud -- Managing resources using GCP Cloud Shell -- GCP networking -- virtual private clouds -- GCP organization structure -- The GCP resource hierarchy -- GCP projects | |
| 505 | 8 | |a GCP Identity and Access Management -- Authentication -- Authorization -- Auditing or accounting -- Service account -- GCP compute services -- GCE virtual machines -- Load balancers and managed instance groups -- Containers and Google Kubernetes Engine -- GCP Cloud Run -- GCP Cloud Functions -- GCP storage and database service spectrum -- GCP storage -- Google Cloud SQL -- Google Cloud Spanner -- Cloud Firestore -- Google Cloud Bigtable -- GCP big data and analytics services -- Google Cloud Dataproc -- Google Cloud Dataflow -- Google Cloud BigQuery -- Google Cloud Pub/Sub | |
| 505 | 8 | |a GCP artificial intelligence services -- Google Vertex AI -- Google Cloud ML APIs -- Summary -- Further reading -- Chapter 2: Mastering Python Programming -- Technical requirements -- The basics of Python -- Basic Python variables and operations -- Basic Python data structure -- Python conditions and loops -- Python functions -- Opening and closing files in Python -- An interesting problem -- Python data libraries and packages -- NumPy -- Pandas -- Matplotlib -- Seaborn -- Summary -- Further reading -- Part 2: Introducing Machine Learning -- Chapter 3: Preparing for ML Development | |
| 505 | 8 | |a Starting from business requirements -- Defining ML problems -- Is ML the best solution? -- ML problem categories -- ML model inputs and outputs -- Measuring ML solutions and data readiness -- ML model performance measurement -- Data readiness -- Collecting data -- Data engineering -- Data sampling and balancing -- Numerical value transformation -- Categorical value transformation -- Missing value handling -- Outlier processing -- Feature engineering -- Feature selection -- Feature synthesis -- Summary -- Further reading -- Chapter 4: Developing and Deploying ML Models -- Splitting the dataset | |
| 505 | 8 | |a Preparing the platform -- Training the model -- Linear regression -- Binary classification -- Support vector machine -- Decision tree and random forest -- Validating the model -- Model validation -- Confusion matrix -- ROC curve and AUC -- More classification metrics -- Tuning the model -- Overfitting and underfitting -- Regularization -- Hyperparameter tuning -- Testing and deploying the model -- Practicing model development with scikit-learn -- Summary -- Further reading -- Chapter 5: Understanding Neural Networks and Deep Learning -- Neural networks and DL -- The cost function | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Cloud computing |x Examinations |v Study guides. | |
| 650 | 0 | |a Computing platforms |x Examinations |v Study guides. | |
| 650 | 0 | |a Computer engineers |x Certification. | |
| 650 | 0 | |a Information technology |x Management. | |
| 650 | 6 | |a Infonuagique |x Examens |v Guides de l'étudiant. | |
| 650 | 6 | |a Plateformes (Informatique) |x Examens |v Guides de l'étudiant. | |
| 650 | 6 | |a Technologie de l'information |x Gestion. | |
| 650 | 7 | |a Information technology |x Management |2 fast | |
| 655 | 7 | |a Study guides |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Song, Logan |t Journey to Become a Google Cloud Machine Learning Engineer |d Birmingham : Packt Publishing, Limited,c2022 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781803233727/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL7080422 | ||
| 938 | |a EBSCOhost |b EBSC |n 3376878 | ||
| 994 | |a 92 |b IZTAP | ||