AWS certified machine learning specialty, MLS-C01 certification guide the definitive guide to passing the MLS-C01 exam on the very first attempt
The AWS Certified Machine Learning Specialty 2020 Certification Guide covers everything you need to pass the MLS-C01 certification exam and serves as a handy, on-the-job reference guide. You'll also find the book useful if you're looking to get up to speed with AWS services for machine lea...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, England :
Packt Publishing, Limited
[2021]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631703906719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Section 1: Introduction to Machine Learning
- Chapter 1: Machine Learning Fundamentals
- 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
- Modeling expectations
- Introducing ML frameworks
- ML in the cloud
- Summary
- Questions
- Chapter 2: AWS Application Services for AI/ML
- Technical requirements
- Analyzing images and videos with Amazon Rekognition
- Exploring the benefits of Amazon Rekognition
- Getting hands-on with Amazon Rekognition
- Text to speech with Amazon Polly
- Exploring the benefits of Amazon Polly
- Getting hands-on with Amazon Polly
- Speech to text with Amazon Transcribe
- Exploring the benefits of Amazon Transcribe
- Getting hands-on with Amazon Transcribe
- Implementing natural language processing with Amazon Comprehend
- Exploring the benefits of Amazon Comprehend
- Getting hands-on with Amazon Comprehend
- Translating documents with Amazon Translate
- Exploring the benefits of Amazon Translate
- Getting hands-on with Amazon Translate
- Extracting text from documents with Amazon Textract
- Exploring the benefits of Amazon Textract
- Getting hands-on with Amazon Textract
- Creating chatbots on Amazon Lex
- Exploring the benefits of Amazon Lex
- Getting hands-on with Amazon Lex
- Summary
- Questions
- Answers
- Section 2: Data Engineering and Exploratory Data Analysis
- Chapter 3: 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
- Questions
- Chapter 4: Understanding and Visualizing Data
- Visualizing relationships in your data
- Visualizing comparisons in your data
- Visualizing distributions in your data
- Visualizing compositions in your data
- Building key performance indicators
- Introducing Quick Sight
- Summary
- Questions
- Chapter 5: AWS Services for Data Storing
- 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
- Relational Database Services (RDSes)
- Managing failover in Amazon RDS
- Taking automatic backup, 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
- Questions
- Answers
- Chapter 6: AWS Services for Data 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
- Processing stored data on AWS
- AWS EMR
- AWS Batch
- Summary
- Questions
- Answers
- Section 3: Data Modeling
- Chapter 7: Applying Machine Learning Algorithms
- Introducing this chapter
- Storing the training data
- A word about ensemble models
- Supervised learning
- Working with regression models
- Working with classification models
- Forecasting models
- Object2Vec
- Unsupervised learning
- Clustering
- Anomaly detection
- Dimensionality reduction
- IP Insights
- Textual analysis
- Blazing Text algorithm
- Sequence-to-sequence algorithm
- Neural Topic Model (NTM) algorithm
- Image processing
- Image classification algorithm
- Semantic segmentation algorithm
- Object detection algorithm
- Summary
- Questions
- Chapter 8: Evaluating and Optimizing Models
- Introducing model evaluation
- Evaluating classification models
- Extracting metrics from a confusion matrix
- Summarizing precision and recall
- Evaluating regression models
- Exploring other regression metrics
- Model optimization
- Grid search
- Summary
- Questions
- Chapter 9: Amazon SageMaker Modeling
- Technical requirements
- Creating notebooks in Amazon SageMaker
- What is Amazon SageMaker?
- Getting hands-on with Amazon SageMaker notebook instances
- Getting hands-on with Amazon SageMaker's training and inference instances
- Model tuning
- Tracking your training jobs and selecting the best model
- Choosing instance types in Amazon SageMaker.
- Choosing the right instance type for a training job
- Choosing the right instance type for an inference job
- Securing SageMaker notebooks
- Creating alternative pipelines with Lambda Functions
- Creating and configuring a Lambda Function
- Completing your configurations and deploying a Lambda Function
- Working with Step Functions
- Summary
- Questions
- Answers
- Why subscribe?
- About Packt
- Other Books You May Enjoy
- Index.
