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

Live Virtual Class

Online Class

Private Class

Public Class

Key points about this course

Duration : 2 Days
Public Class : RM4,500.00
Live Virtual Class : RM4,500.00
HRDF Claimable
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.

This course is available on face to face training, live virtual class training or online training.

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

Introduction

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

Modeling

  • 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

 

Exam Details

Format
Multiple choice, multiple answer

Type
Specialty

Delivery Method
Testing center or online proctored exam

Time
180 minutes to complete the exam

Cost
300 USD (Practice exam: 40 USD)

Language
Available in English, Japanese, Korean, and Simplified Chinese

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