book

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

by Somanath Nanda, Weslley Moura

March 2021

Intermediate to advanced

338 pages

7h 11m

English

Packt Publishing

Read now

Unlock full access

ContributorsAbout the authorsAbout the reviewer
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Comparing AI, ML, and DLExamining MLExamining DLClassifying supervised, unsupervised, and reinforcement learning Introducing supervised learningThe CRISP-DM modeling life cycleData splittingOverfitting and underfittingApplying cross-validation and measuring overfitting Bootstrapping methodsThe variance versus bias trade-off Shuffling your training setModeling expectationsIntroducing ML frameworksML in the cloudSummaryQuestions
Technical requirementsAnalyzing images and videos with Amazon RekognitionExploring the benefits of Amazon RekognitionGetting hands-on with Amazon RekognitionText to speech with Amazon PollyExploring the benefits of Amazon PollyGetting hands-on with Amazon PollySpeech to text with Amazon TranscribeExploring the benefits of Amazon TranscribeGetting hands-on with Amazon TranscribeImplementing natural language processing with Amazon Comprehend Exploring the benefits of Amazon ComprehendGetting hands-on with Amazon ComprehendTranslating documents with Amazon TranslateExploring the benefits of Amazon TranslateGetting hands-on with Amazon TranslateExtracting text from documents with Amazon TextractExploring the benefits of Amazon TextractGetting hands-on with Amazon TextractCreating chatbots on Amazon Lex Exploring the benefits of Amazon LexGetting hands-on with Amazon LexSummaryQuestionsAnswers
Identifying types of featuresDealing with categorical featuresTransforming nominal featuresApplying binary encodingTransforming ordinal featuresAvoiding confusion in our train and test datasetsDealing with numerical featuresData normalization Data standardizationApplying binning and discretizationApplying other types of numerical transformationsUnderstanding data distributionsHandling missing valuesDealing with outliers Dealing with unbalanced datasetsDealing with text dataBag of wordsTF-IDFWord embeddingSummaryQuestions
Visualizing relationships in your dataVisualizing comparisons in your dataVisualizing distributions in your dataVisualizing compositions in your dataBuilding key performance indicatorsIntroducing Quick SightSummaryQuestions
Technical requirementsStoring data on Amazon S3Creating buckets to hold dataDistinguishing between object tags and object metadataControlling access to buckets and objects on Amazon S3 S3 bucket policyProtecting data on Amazon S3Applying bucket versioningApplying encryption to bucketsSecuring S3 objects at rest and in transit Using other types of data storesRelational Database Services (RDSes)Managing failover in Amazon RDSTaking automatic backup, RDS snapshots, and restore and read replicasWriting to Amazon Aurora with multi-master capabilitiesStoring columnar data on Amazon RedshiftAmazon DynamoDB for NoSQL database as a serviceSummaryQuestionsAnswers
Technical requirementsCreating ETL jobs on AWS GlueFeatures of AWS GlueGetting hands-on with AWS Glue data catalog componentsGetting hands-on with AWS Glue ETL componentsQuerying S3 data using AthenaProcessing real-time data using Kinesis data streamsStoring and transforming real-time data using Kinesis Data FirehoseDifferent ways of ingesting data from on-premises into AWSAWS Storage GatewaySnowball, Snowball Edge, and SnowmobileAWS DataSyncProcessing stored data on AWSAWS EMRAWS BatchSummaryQuestionsAnswers

Introducing this chapterStoring the training dataA word about ensemble modelsSupervised learningWorking with regression modelsWorking with classification modelsForecasting modelsObject2VecUnsupervised learningClusteringAnomaly detectionDimensionality reductionIP InsightsTextual analysisBlazing Text algorithmSequence-to-sequence algorithmNeural Topic Model (NTM) algorithmImage processingImage classification algorithmSemantic segmentation algorithmObject detection algorithmSummaryQuestions
Introducing model evaluationEvaluating classification modelsExtracting metrics from a confusion matrixSummarizing precision and recallEvaluating regression modelsExploring other regression metricsModel optimizationGrid searchSummaryQuestions
Technical requirementsCreating notebooks in Amazon SageMakerWhat is Amazon SageMaker?Getting hands-on with Amazon SageMaker notebook instancesGetting hands-on with Amazon SageMaker's training and inference instancesModel tuningTracking your training jobs and selecting the best modelChoosing instance types in Amazon SageMakerChoosing the right instance type for a training jobChoosing the right instance type for an inference jobSecuring SageMaker notebooksCreating alternative pipelines with Lambda FunctionsCreating and configuring a Lambda FunctionCompleting your configurations and deploying a Lambda Function Working with Step FunctionsSummaryQuestionsAnswers
Other Books You May EnjoyPackt is searching for authors like youLeave a review - let other readers know what you think

Content preview from AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

Section 3: Data Modeling

This section summarizes the most common machine learning algorithms. It describes the logic of each algorithm, when to use each of them, and how to tune their hyperparameters.

This section contains the following chapters: