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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) |
Tabla de Contenidos:
- 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
- Summary
- Further reading
- 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
- Summary
- Further reading
- 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
- Summary
- Further reading
- Chapter 5: Understanding Neural Networks and Deep Learning
- Neural networks and DL
- The cost function