AWS Certified Machine Learning Specialty (MLS-C01) certification guide the ultimate guide to passing the MLS-C01 exam on your first attempt
Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices--PCs, tablets, and smartphones. Throughout the book, you'll le...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing Ltd.
2024
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| Edition: | Second edition |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Exam Readiness Drill
- Chapter Review Questions
- Relational Database Service (RDS)
- Managing failover in Amazon RDS
- Taking automatic backups, RDS snapshots, and restore and read replicas
- Writing to Amazon Aurora with multi-master capabilities
- Storing columnar data on Amazon Redshift
- Amazon DynamoDB for NoSQL Database-as-a-Service
- Summary
- Exam Readiness Drill
- Chapter Review Questions
- Chapter 3: AWS Services for Data Migration and Processing
- Technical requirements
- Creating ETL jobs on AWS Glue
- Features of AWS Glue
- Getting hands-on with AWS Glue Data Catalog components
- Getting hands-on with AWS Glue ETL components
- Querying S3 data using Athena
- Processing real-time data using Kinesis Data Streams
- Storing and transforming real-time data using Kinesis Data Firehose
- Different ways of ingesting data from on-premises into AWS
- AWS Storage Gateway
- Snowball, Snowball Edge, and Snowmobile
- AWS DataSync
- AWS Database Migration Service
- Processing stored data on AWS
- AWS EMR
- AWS Batch
- Summary
- Exam Readiness Drill
- Chapter Review Questions
- Chapter 4: Data Preparation and Transformation
- Identifying types of features
- Dealing with categorical features
- Transforming nominal features
- Applying binary encoding
- Transforming ordinal features
- Avoiding confusion in our train and test datasets
- Dealing with numerical features
- Data normalization
- Data standardization
- Applying binning and discretization
- Applying other types of numerical transformations
- Understanding data distributions
- Handling missing values
- Dealing with outliers
- Dealing with unbalanced datasets
- Dealing with text data
- Bag of words
- TF-IDF
- Word embedding
- Summary
- Modeling expectations
- Introducing ML frameworks
- ML in the cloud
- Summary
- Exam Readiness Drill
- Chapter Review Questions
- Chapter 2: AWS Services for Data Storage
- Technical requirements
- Storing Data on Amazon S3
- Creating buckets to hold data
- Distinguishing between object tags and object metadata
- Controlling access to buckets and objects on amazon s3
- S3 bucket policy
- Protecting data on amazon s3
- Applying bucket versioning
- Applying encryption to buckets
- Securing s3 objects at rest and in transit
- Using other types of data stores
- Cover
- FM
- Copyright
- Contributors
- Table of Contents
- Preface
- Chapter 1: Machine Learning Fundamentals
- Making the Most Out of this Book
- Your Certification and Beyond
- Comparing AI, ML, and DL
- Examining ML
- Examining DL
- Classifying supervised, unsupervised, and reinforcement learning
- Introducing supervised learning
- The CRISP-DM modeling life cycle
- Data splitting
- Overfitting and underfitting
- Applying cross-validation and measuring overfitting
- Bootstrapping methods
- The variance versus bias trade-off
- Shuffling your training set