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Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.
| Lectures | Duration |
|---|---|
| 1. Course Introduction: What to Expect | 6m |
| Lectures | Duration |
|---|---|
| 1. Section Intro: Data Engineering | 1m |
| 2. Amazon S3 - Overview | 5m |
| 3. Amazon S3 - Storage Tiers & Lifecycle Rules | 4m |
| 4. Amazon S3 Security | 8m |
| 5. Kinesis Data Streams & Kinesis Data Firehose | 9m |
| 6. Lab 1.1 - Kinesis Data Firehose | 6m |
| 7. Kinesis Data Analytics | 4m |
| 8. Lab 1.2 - Kinesis Data Analytics | 7m |
| 9. Kinesis Video Streams | 3m |
| 10. Kinesis ML Summary | 1m |
| 11. Glue Data Catalog & Crawlers | 3m |
| 12. Lab 1.3 - Glue Data Catalog | 4m |
| 13. Glue ETL | 2m |
| 14. Lab 1.4 - Glue ETL | 6m |
| 15. Lab 1.5 - Athena | 1m |
| 16. Lab 1 - Cleanup | 2m |
| 17. AWS Data Stores in Machine Learning | 3m |
| 18. AWS Data Pipelines | 3m |
| 19. AWS Batch | 2m |
| 20. AWS DMS - Database Migration Services | 2m |
| 21. AWS Step Functions | 3m |
| 22. Full Data Engineering Pipelines | 5m |
| Lectures | Duration |
|---|---|
| 1. Section Intro: Data Analysis | 1m |
| 2. Python in Data Science and Machine Learning | 12m |
| 3. Example: Preparing Data for Machine Learning in a Jupyter Notebook. | 10m |
| 4. Types of Data | 5m |
| 5. Data Distributions | 6m |
| 6. Time Series: Trends and Seasonality | 4m |
| 7. Introduction to Amazon Athena | 5m |
| 8. Overview of Amazon Quicksight | 6m |
| 9. Types of Visualizations, and When to Use Them. | 5m |
| 10. Elastic MapReduce (EMR) and Hadoop Overview | 7m |
| 11. Apache Spark on EMR | 10m |
| 12. EMR Notebooks, Security, and Instance Types | 4m |
| 13. Feature Engineering and the Curse of Dimensionality | 7m |
| 14. Imputing Missing Data | 8m |
| 15. Dealing with Unbalanced Data | 6m |
| 16. Handling Outliers | 9m |
| 17. Binning, Transforming, Encoding, Scaling, and Shuffling | 8m |
| 18. Amazon SageMaker Ground Truth and Label Generation | 4m |
| 19. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 1 | 6m |
| 20. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2 | 10m |
| 21. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3 | 14m |
| Lectures | Duration |
|---|---|
| 1. Section Intro: Modeling | 2m |
| 2. Introduction to Deep Learning | 9m |
| 3. Convolutional Neural Networks | 12m |
| 4. Recurrent Neural Networks | 11m |
| 5. Deep Learning on EC2 and EMR | 2m |
| 6. Tuning Neural Networks | 5m |
| 7. Regularization Techniques for Neural Networks (Dropout, Early Stopping) | 7m |
| 8. Grief with Gradients: The Vanishing Gradient problem | 4m |
| 9. L1 and L2 Regularization | 3m |
| 10. The Confusion Matrix | 6m |
| 11. Precision, Recall, F1, AUC, and more | 7m |
| 12. Ensemble Methods: Bagging and Boosting | 4m |
| 13. Introducing Amazon SageMaker | 8m |
| 14. Linear Learner in SageMaker | 5m |
| 15. XGBoost in SageMaker | 3m |
| 16. Seq2Seq in SageMaker | 5m |
| 17. DeepAR in SageMaker | 4m |
| 18. BlazingText in SageMaker | 5m |
| 19. Object2Vec in SageMaker | 5m |
| 20. Object Detection in SageMaker | 4m |
| 21. Image Classification in SageMaker | 4m |
| 22. Semantic Segmentation in SageMaker | 4m |
| 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 | 3m |
| 27. K-Means Clustering in SageMaker | 5m |
| 28. Principal Component Analysis (PCA) in SageMaker | 3m |
| 29. Factorization Machines in SageMaker | 4m |
| 30. IP Insights in SageMaker | 3m |
| 31. Reinforcement Learning in SageMaker | 12m |
| 32. Automatic Model Tuning | 6m |
| 33. Apache Spark with SageMaker | 3m |
| 34. Amazon Comprehend | 6m |
| 35. Amazon Translate | 2m |
| 36. Amazon Transcribe | 4m |
| 37. Amazon Polly | 6m |
| 38. Amazon Rekognition | 7m |
| 39. Amazon Forecast | 2m |
| 40. Amazon Lex | 3m |
| 41. The Best of the Rest: Other High-Level AWS Machine Learning Services | 3m |
| 42. Putting them All Together | 2m |
| 43. Lab: Tuning a Convolutional Neural Network on EC2, Part 1 | 9m |
| 44. Lab: Tuning a Convolutional Neural Network on EC2, Part 2 | 9m |
| 45. Lab: Tuning a Convolutional Neural Network on EC2, Part 3 | 6m |
| Lectures | Duration |
|---|---|
| 1. Section Intro: Machine Learning Implementation and Operations | 1m |
| 2. SageMaker's Inner Details and Production Variants | 11m |
| 3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass | 4m |
| 4. SageMaker Security: Encryption at Rest and In Transit | 5m |
| 5. SageMaker Security: VPC's, IAM, Logging, and Monitoring | 4m |
| 6. SageMaker Resource Management: Instance Types and Spot Training | 4m |
| 7. SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's | 5m |
| 8. SageMaker Inference Pipelines | 2m |
| 9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1 | 5m |
| 10. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2 | 11m |
| 11. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3 | 12m |
| Lectures | Duration |
|---|---|
| 1. Section Intro: Wrapping Up | 1m |
| 2. More Preparation Resources | 6m |
| 3. Test-Taking Strategies, and What to Expect | 10m |
| 4. You Made It! | 1m |
| 5. Save 50% on your AWS Exam Cost! | 2m |
| 6. Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only | 1m |
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