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Grazie al nostro supporto la percentuale di candidati che ottengono la certificazione al primo tentativo è prossima al 100%.
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RICERCA CORSI
08-KLA-ATRWS - Network Analysis and Troubleshooting Through Wireshark (ATRWS) 
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Machine Learning
AWS SPECIALITY AWS, AWS-MLEARN, CLOUD, Specialty
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OBJECTIVES
This course is structured into four domains: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations.
PREREQUISITES
Some existing familiarity with machine learning
An AWS account is needed to perform the hands-on lab exercises
WHO SHOULD ATTEND
Individuals performing a development or data science role seeking certification in machine learning and AWS.
TOPICS
S3 data lakes
AWS Glue and Glue ETL
Kinesis data streams, firehose, and video streams
DynamoDB
Data Pipelines, AWS Batch, and Step Functions
Using scikit_learn
Data science basics
Athena and Quicksight
Elastic MapReduce (EMR)
Apache Spark and MLLib
Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
Ground Truth
Deep Learning basics
Tuning neural networks and avoiding overfitting
Amazon SageMaker, in depth
Regularization techniques
Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
Security best practices with machine learning on AWS
This course is structured into four domains: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations.
PREREQUISITES
Some existing familiarity with machine learning
An AWS account is needed to perform the hands-on lab exercises
WHO SHOULD ATTEND
Individuals performing a development or data science role seeking certification in machine learning and AWS.
TOPICS
S3 data lakes
AWS Glue and Glue ETL
Kinesis data streams, firehose, and video streams
DynamoDB
Data Pipelines, AWS Batch, and Step Functions
Using scikit_learn
Data science basics
Athena and Quicksight
Elastic MapReduce (EMR)
Apache Spark and MLLib
Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
Ground Truth
Deep Learning basics
Tuning neural networks and avoiding overfitting
Amazon SageMaker, in depth
Regularization techniques
Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
Security best practices with machine learning on AWS
