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...
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing Ltd.
2022
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| Edition: | [First edition] |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- Includes bibliographical references and index
- 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
- 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
- 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