Journey to become a Google Cloud machine learning engineer : build the mind and hand of a Google certified ML professional / Logan Song.

Format:

Book

Author/Creator:

Song, Logan, author.

Language:

English

Subjects (All):

Cloud computing--Examinations--Study guides.

Cloud computing.

Computing platforms--Examinations--Study guides.

Computing platforms.

Computer engineers--Certification.

Computer engineers.

Information technology--Management.

Information technology.

Physical Description:

1 online resource (330 pages)

Edition:

[First edition].

Place of Publication:

Birmingham : Packt Publishing, Limited, [2022]

System Details:

Mode of access: World Wide Web.

Biography/History:

Song Dr. Logan: Dr. Logan Song is the enterprise cloud director and chief cloud architect at Dito. With 25+ years of professional experience, Dr. Song is highly skilled in enterprise information technologies, specializing in cloud computing and machine learning. He is a Google Cloud-certified professional solution architect and machine learning engineer, an AWS-certified professional solution architect and machine learning specialist, and a Microsoft-certified Azure solution architect expert. Dr. Song holds a Ph. D. in industrial engineering, an MS in computer science, and an ME in management engineering. Currently, he is also an adjunct professor at the University of Texas at Dallas, teaching cloud computing and machine learning courses.

Summary:

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.

Contents:

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

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

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

Part 2: Introducing Machine Learning

Chapter 3: Preparing for ML Development

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

Chapter 4: Developing and Deploying ML Models

Splitting the dataset

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

Chapter 5: Understanding Neural Networks and Deep Learning

Neural networks and DL

The cost function

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781803239415

1803239417

OCLC:

1346155425