AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01)

1m 1. Section Intro: Data Engineering 5m 2. Amazon S3 - Overview 4m 3. Amazon S3 - Storage Tiers & Lifecycle Rules 8m 4. Amazon S3 Security 9m 5. Kinesis Data Streams & Kinesis Data Firehose 6m 6. Lab 1.1 - Kinesis Data Firehose 4m 7. Kinesis Data Analytics 7m 8. Lab 1.2 - Kinesis Data Analytics 3m 9. Kinesis Video Streams 1m 10. Kinesis ML Summary 3m 11. Glue Data Catalog & Crawlers 4m 12. Lab 1.3 - Glue Data Catalog 2m 13. Glue ETL 6m 14. Lab 1.4 - Glue ETL 1m 15. Lab 1.5 - Athena 2m 16. Lab 1 - Cleanup 3m 17. AWS Data Stores in Machine Learning 3m 18. AWS Data Pipelines 2m 19. AWS Batch 2m 20. AWS DMS - Database Migration Services 3m 21. AWS Step Functions 5m 22. Full Data Engineering Pipelines

1m 1. Section Intro: Data Analysis 12m 2. Python in Data Science and Machine Learning 10m 3. Example: Preparing Data for Machine Learning in a Jupyter Notebook. 5m 4. Types of Data 6m 5. Data Distributions 4m 6. Time Series: Trends and Seasonality 5m 7. Introduction to Amazon Athena 6m 8. Overview of Amazon Quicksight 5m 9. Types of Visualizations, and When to Use Them. 7m 10. Elastic MapReduce (EMR) and Hadoop Overview 10m 11. Apache Spark on EMR 4m 12. EMR Notebooks, Security, and Instance Types 7m 13. Feature Engineering and the Curse of Dimensionality 8m 14. Imputing Missing Data 6m 15. Dealing with Unbalanced Data 9m 16. Handling Outliers 8m 17. Binning, Transforming, Encoding, Scaling, and Shuffling 4m 18. Amazon SageMaker Ground Truth and Label Generation 6m 19. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 1 10m 20. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2 14m 21. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3

2m 1. Section Intro: Modeling 9m 2. Introduction to Deep Learning 12m 3. Convolutional Neural Networks 11m 4. Recurrent Neural Networks 2m 5. Deep Learning on EC2 and EMR 5m 6. Tuning Neural Networks 7m 7. Regularization Techniques for Neural Networks (Dropout, Early Stopping) 4m 8. Grief with Gradients: The Vanishing Gradient problem 3m 9. L1 and L2 Regularization 6m 10. The Confusion Matrix 7m 11. Precision, Recall, F1, AUC, and more 4m 12. Ensemble Methods: Bagging and Boosting 8m 13. Introducing Amazon SageMaker 5m 14. Linear Learner in SageMaker 3m 15. XGBoost in SageMaker 5m 16. Seq2Seq in SageMaker 4m 17. DeepAR in SageMaker 5m 18. BlazingText in SageMaker 5m 19. Object2Vec in SageMaker 4m 20. Object Detection in SageMaker 4m 21. Image Classification in SageMaker 4m 22. Semantic Segmentation in SageMaker 3m 23. Random Cut Forest in SageMaker 3m 24. Neural Topic Model in SageMaker 3m 25. Latent Dirichlet Allocation (LDA) in SageMaker 3m 26. K-Nearest-Neighbors (KNN) in SageMaker 5m 27. K-Means Clustering in SageMaker 3m 28. Principal Component Analysis (PCA) in SageMaker 4m 29. Factorization Machines in SageMaker 3m 30. IP Insights in SageMaker 12m 31. Reinforcement Learning in SageMaker 6m 32. Automatic Model Tuning 3m 33. Apache Spark with SageMaker 6m 34. Amazon Comprehend 2m 35. Amazon Translate 4m 36. Amazon Transcribe 6m 37. Amazon Polly 7m 38. Amazon Rekognition 2m 39. Amazon Forecast 3m 40. Amazon Lex 3m 41. The Best of the Rest: Other High-Level AWS Machine Learning Services 2m 42. Putting them All Together 9m 43. Lab: Tuning a Convolutional Neural Network on EC2, Part 1 9m 44. Lab: Tuning a Convolutional Neural Network on EC2, Part 2 6m 45. Lab: Tuning a Convolutional Neural Network on EC2, Part 3

1m 1. Section Intro: Machine Learning Implementation and Operations 11m 2. SageMaker's Inner Details and Production Variants 4m 3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass 5m 4. SageMaker Security: Encryption at Rest and In Transit 4m 5. SageMaker Security: VPC's, IAM, Logging, and Monitoring 4m 6. SageMaker Resource Management: Instance Types and Spot Training 5m 7. SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's 2m 8. SageMaker Inference Pipelines 5m 9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1 11m 10. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2 12m 11. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3

1m 1. Section Intro: Wrapping Up 6m 2. More Preparation Resources 10m 3. Test-Taking Strategies, and What to Expect 1m 4. You Made It! 2m 5. Save 50% on your AWS Exam Cost! 1m 6. Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only