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