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

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Introduction
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1. Course Introduction: What to Expect6m
Data Engineering
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1. Section Intro: Data Engineering1m
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2. Amazon S3 - Overview5m
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3. Amazon S3 - Storage Tiers & Lifecycle Rules4m
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4. Amazon S3 Security8m
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5. Kinesis Data Streams & Kinesis Data Firehose9m
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6. Lab 1.1 - Kinesis Data Firehose6m
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7. Kinesis Data Analytics4m
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8. Lab 1.2 - Kinesis Data Analytics7m
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9. Kinesis Video Streams3m
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10. Kinesis ML Summary1m
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11. Glue Data Catalog & Crawlers3m
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12. Lab 1.3 - Glue Data Catalog4m
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13. Glue ETL2m
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14. Lab 1.4 - Glue ETL6m
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15. Lab 1.5 - Athena1m
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16. Lab 1 - Cleanup2m
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17. AWS Data Stores in Machine Learning3m
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18. AWS Data Pipelines3m
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19. AWS Batch2m
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20. AWS DMS - Database Migration Services2m
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21. AWS Step Functions3m
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22. Full Data Engineering Pipelines5m
Exploratory Data Analysis
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1. Section Intro: Data Analysis1m
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2. Python in Data Science and Machine Learning12m
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3. Example: Preparing Data for Machine Learning in a Jupyter Notebook.10m
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4. Types of Data5m
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5. Data Distributions6m
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6. Time Series: Trends and Seasonality4m
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7. Introduction to Amazon Athena5m
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8. Overview of Amazon Quicksight6m
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9. Types of Visualizations, and When to Use Them.5m
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10. Elastic MapReduce (EMR) and Hadoop Overview7m
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11. Apache Spark on EMR10m
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12. EMR Notebooks, Security, and Instance Types4m
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13. Feature Engineering and the Curse of Dimensionality7m
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14. Imputing Missing Data8m
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15. Dealing with Unbalanced Data6m
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16. Handling Outliers9m
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17. Binning, Transforming, Encoding, Scaling, and Shuffling8m
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18. Amazon SageMaker Ground Truth and Label Generation4m
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19. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 16m
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20. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 210m
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21. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 314m
Modeling
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1. Section Intro: Modeling2m
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2. Introduction to Deep Learning9m
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3. Convolutional Neural Networks12m
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4. Recurrent Neural Networks11m
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5. Deep Learning on EC2 and EMR2m
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6. Tuning Neural Networks5m
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7. Regularization Techniques for Neural Networks (Dropout, Early Stopping)7m
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8. Grief with Gradients: The Vanishing Gradient problem4m
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9. L1 and L2 Regularization3m
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10. The Confusion Matrix6m
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11. Precision, Recall, F1, AUC, and more7m
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12. Ensemble Methods: Bagging and Boosting4m
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13. Introducing Amazon SageMaker8m
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14. Linear Learner in SageMaker5m
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15. XGBoost in SageMaker3m
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16. Seq2Seq in SageMaker5m
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17. DeepAR in SageMaker4m
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18. BlazingText in SageMaker5m
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19. Object2Vec in SageMaker5m
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20. Object Detection in SageMaker4m
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21. Image Classification in SageMaker4m
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22. Semantic Segmentation in SageMaker4m
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23. Random Cut Forest in SageMaker3m
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24. Neural Topic Model in SageMaker3m
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25. Latent Dirichlet Allocation (LDA) in SageMaker3m
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26. K-Nearest-Neighbors (KNN) in SageMaker3m
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27. K-Means Clustering in SageMaker5m
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28. Principal Component Analysis (PCA) in SageMaker3m
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29. Factorization Machines in SageMaker4m
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30. IP Insights in SageMaker3m
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31. Reinforcement Learning in SageMaker12m
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32. Automatic Model Tuning6m
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33. Apache Spark with SageMaker3m
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34. Amazon Comprehend6m
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35. Amazon Translate2m
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36. Amazon Transcribe4m
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37. Amazon Polly6m
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38. Amazon Rekognition7m
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39. Amazon Forecast2m
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40. Amazon Lex3m
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41. The Best of the Rest: Other High-Level AWS Machine Learning Services3m
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42. Putting them All Together2m
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43. Lab: Tuning a Convolutional Neural Network on EC2, Part 19m
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44. Lab: Tuning a Convolutional Neural Network on EC2, Part 29m
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45. Lab: Tuning a Convolutional Neural Network on EC2, Part 36m
ML Implementation and Operations
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1. Section Intro: Machine Learning Implementation and Operations1m
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2. SageMaker's Inner Details and Production Variants11m
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3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass4m
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4. SageMaker Security: Encryption at Rest and In Transit5m
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5. SageMaker Security: VPC's, IAM, Logging, and Monitoring4m
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6. SageMaker Resource Management: Instance Types and Spot Training4m
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7. SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's5m
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8. SageMaker Inference Pipelines2m
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9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 15m
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10. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 211m
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11. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 312m
Wrapping Up
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1. Section Intro: Wrapping Up1m
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2. More Preparation Resources6m
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3. Test-Taking Strategies, and What to Expect10m
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4. You Made It!1m
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5. Save 50% on your AWS Exam Cost!2m
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6. Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only1m