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

Best Seller 106 Lectures 9h 8m
Prepare for your Amazon AWS examination with our training course. The AWS-Certified-Machine-Learning-Specialty-MLS-C01 course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Amazon AWS certification exam. Pass the Amazon AWS AWS-Certified-Machine-Learning-Specialty-MLS-C01 test with flying colors.
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Curriculum For This Course

  • 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
  • 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
  • 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
  • 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
  • 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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