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Key points about this course

Duration : 2 Days
Course Fee : RM2,599.00

HRD Corp Claimable Course

AWS Certified Machine Learning Specialty
Exam Code : AWS Certified Machine Learning - Specialty

Live Virtual Class

Public Class

In-House Training

Private Class

Course Overview

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

Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.

Course Prerequisites
  • Associate-level knowledge of AWS services such as EC2
  • Some existing familiarity with machine learning
  • An AWS account is needed to perform the hands-on lab exercises
Course Objectives
  • 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
Course Content


Data Engineering

  • Intro: Data Engineering
  • Amazon S3 - Overview
  • Amazon S3 - Storage Tiers & Lifecycle Rules
  • Amazon S3 Security
  • Kinesis Data Streams & Kinesis Data Firehose
  • Kinesis Data Analytics
  • Kinesis Video Streams
  • Kinesis ML Summary
  • Glue Data Catalog & Crawlers
  • Glue ETL
  • AWS Data Stores in Machine Learning
  • AWS Data Pipelines
  • AWS Batch
  • AWS DMS - Database Migration Services
  • AWS Step Functions
  • Full Data Engineering Pipelines

Data Engineering

  • Intro: Data Analysis
  • Python in Data Science and Machine Learning
  • Example: Preparing Data for Machine Learning in a Jupyter Notebook.
  • Types of Data
  • Data Distributions
  • Time Series: Trends and Seasonality
  • Introduction to Amazon Athena
  • Overview of Amazon Quicksight
  • Types of Visualizations, and When to Use Them.
  • Elastic MapReduce (EMR) and Hadoop Overview
  • Apache Spark on EMR
  • EMR Notebooks, Security, and Instance Types
  • Feature Engineering and the Curse of Dimensionality
  • Imputing Missing Data
  • Dealing with Unbalanced Data
  • Handling Outliers
  • Binning, Transforming, Encoding, Scaling, and Shuffling
  • Amazon SageMaker Ground Truth and Label Generation


  • Intro: Modeling
  • Introduction to Deep Learning
  • Activation Functions
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Deep Learning on EC2 and EMR
  • Tuning Neural Networks
  • Regularization Techniques for Neural Networks (Dropout, Early Stopping)
  • Grief with Gradients: The Vanishing Gradient problem
  • L1 and L2 Regularization
  • The Confusion Matrix
  • Precision, Recall, F1, AUC, and more
  • Ensemble Methods: Bagging and Boosting
  • Introducing Amazon SageMaker
  • Linear Learner in SageMaker
  • XGBoost in SageMaker
  • Seq2Seq in SageMaker
  • DeepAR in SageMaker
  • BlazingText in SageMaker
  • Object2Vec in SageMaker
  • Object Detection in SageMaker
  • Image Classification in SageMaker
  • Semantic Segmentation in SageMaker
  • Random Cut Forest in SageMaker
  • Neural Topic Model in SageMaker
  • Latent Dirichlet Allocation (LDA) in SageMaker
  • K-Nearest-Neighbors (KNN) in SageMaker
  • K-Means Clustering in SageMaker
  • Principal Component Analysis (PCA) in SageMaker
  • Factorization Machines in SageMaker
  • IP Insights in SageMaker
  • Reinforcement Learning in SageMaker
  • Automatic Model Tuning
  • Apache Spark with SageMaker
  • SageMaker Studio, and new SageMaker features for 2020
  • Amazon Comprehend
  • Amazon Translate
  • Amazon Transcribe
  • Amazon Rekognition
  • Amazon Forecast
  • Amazon Lex
  • The Best of the Rest: Other High-Level AWS Machine Learning Services
  • New ML Services for 2020
  • Putting them All Together

ML Implementation and Operation

  • Intro: Machine Learning Implementation and Operations
  • SageMaker's Inner Details and Productions Variants
  • SageMaker On the Edge: SageMaker Neo and IoT Greengrass
  • SageMaker Security: Encryption at Rest and In Transit
  • SageMaker Security: VPC's, IAM, Logging, and Monitoring
  • SageMaker Resource Management: Instance Types and Spot Training
  • SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's
  • SageMaker Inference Pipelines


About the Certification

Multiple choice, multiple answer


Delivery Method
Testing center or online proctored exam

180 minutes to complete the exam

300 USD (Practice exam: 40 USD)

Available in English, Japanese, Korean, and Simplified Chinese

  • AWS Certified Machine Learning Specialty

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