AWS Certified Machine Learning Specialty 2024 – Hands On!

• What to expect on the AWS Certified Machine Learning Specialty exam
• Amazon SageMaker’s built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
• Feature engineering techniques, including imputation, outliers, binning, and normalization
• High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
• Data engineering with S3, Glue, Kinesis, and DynamoDB
• Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR
• Deep learning and hyperparameter tuning of deep neural networks
• Automatic model tuning and operations with SageMaker
• L1 and L2 regularization
• Applying security best practices to machine learning pipelines 

This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we’ll cover include:

• 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