AWS Certified Machine Learning - Specialty (MLS-C01) Video Course

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

Your Amazon AWS Certified Machine Learning - Specialty exam is just around the corner, right? So, it's high time to find an effective preparation tool! Our training course is what you really need! This is a series of videos led by the experienced IT instructors who will provide you with a detailed overview of the AWS Certified Machine Learning - Specialty certification test. Ace your Amazon AWS Certified Machine Learning - Specialty at the first attempt and obtain the AWS Certified Machine Learning - Specialty (MLS-C01) credential with ease.

# of Students
86
# of Lectures
106
Course Length
9 h
Course Rating
4.5
Price $32.99
Today $29.99

Curriculum for AWS Certified Machine Learning - Specialty Video Course

Introduction

  Video Name Time
  1. Course Introduction: What to Expect 6:00
  Video Name Time
  1. Section Intro: Data Engineering 1:00
  2. Amazon S3 - Overview 5:00
  3. Amazon S3 - Storage Tiers & Lifecycle Rules 4:00
  4. Amazon S3 Security 8:00
  5. Kinesis Data Streams & Kinesis Data Firehose 9:00
  6. Lab 1.1 - Kinesis Data Firehose 6:00
  7. Kinesis Data Analytics 4:00
  8. Lab 1.2 - Kinesis Data Analytics 7:00
  9. Kinesis Video Streams 3:00
  10. Kinesis ML Summary 1:00
  11. Glue Data Catalog & Crawlers 3:00
  12. Lab 1.3 - Glue Data Catalog 4:00
  13. Glue ETL 2:00
  14. Lab 1.4 - Glue ETL 6:00
  15. Lab 1.5 - Athena 1:00
  16. Lab 1 - Cleanup 2:00
  17. AWS Data Stores in Machine Learning 3:00
  18. AWS Data Pipelines 3:00
  19. AWS Batch 2:00
  20. AWS DMS - Database Migration Services 2:00
  21. AWS Step Functions 3:00
  22. Full Data Engineering Pipelines 5:00
  Video Name Time
  1. Section Intro: Data Analysis 1:00
  2. Python in Data Science and Machine Learning 12:00
  3. Example: Preparing Data for Machine Learning in a Jupyter Notebook. 10:00
  4. Types of Data 5:00
  5. Data Distributions 6:00
  6. Time Series: Trends and Seasonality 4:00
  7. Introduction to Amazon Athena 5:00
  8. Overview of Amazon Quicksight 6:00
  9. Types of Visualizations, and When to Use Them. 5:00
  10. Elastic MapReduce (EMR) and Hadoop Overview 7:00
  11. Apache Spark on EMR 10:00
  12. EMR Notebooks, Security, and Instance Types 4:00
  13. Feature Engineering and the Curse of Dimensionality 7:00
  14. Imputing Missing Data 8:00
  15. Dealing with Unbalanced Data 6:00
  16. Handling Outliers 9:00
  17. Binning, Transforming, Encoding, Scaling, and Shuffling 8:00
  18. Amazon SageMaker Ground Truth and Label Generation 4:00
  19. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 1 6:00
  20. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2 10:00
  21. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3 14:00
  Video Name Time
  1. Section Intro: Modeling 2:00
  2. Introduction to Deep Learning 9:00
  3. Convolutional Neural Networks 12:00
  4. Recurrent Neural Networks 11:00
  5. Deep Learning on EC2 and EMR 2:00
  6. Tuning Neural Networks 5:00
  7. Regularization Techniques for Neural Networks (Dropout, Early Stopping) 7:00
  8. Grief with Gradients: The Vanishing Gradient problem 4:00
  9. L1 and L2 Regularization 3:00
  10. The Confusion Matrix 6:00
  11. Precision, Recall, F1, AUC, and more 7:00
  12. Ensemble Methods: Bagging and Boosting 4:00
  13. Introducing Amazon SageMaker 8:00
  14. Linear Learner in SageMaker 5:00
  15. XGBoost in SageMaker 3:00
  16. Seq2Seq in SageMaker 5:00
  17. DeepAR in SageMaker 4:00
  18. BlazingText in SageMaker 5:00
  19. Object2Vec in SageMaker 5:00
  20. Object Detection in SageMaker 4:00
  21. Image Classification in SageMaker 4:00
  22. Semantic Segmentation in SageMaker 4:00
  23. Random Cut Forest in SageMaker 3:00
  24. Neural Topic Model in SageMaker 3:00
  25. Latent Dirichlet Allocation (LDA) in SageMaker 3:00
  26. K-Nearest-Neighbors (KNN) in SageMaker 3:00
  27. K-Means Clustering in SageMaker 5:00
  28. Principal Component Analysis (PCA) in SageMaker 3:00
  29. Factorization Machines in SageMaker 4:00
  30. IP Insights in SageMaker 3:00
  31. Reinforcement Learning in SageMaker 12:00
  32. Automatic Model Tuning 6:00
  33. Apache Spark with SageMaker 3:00
  34. Amazon Comprehend 6:00
  35. Amazon Translate 2:00
  36. Amazon Transcribe 4:00
  37. Amazon Polly 6:00
  38. Amazon Rekognition 7:00
  39. Amazon Forecast 2:00
  40. Amazon Lex 3:00
  41. The Best of the Rest: Other High-Level AWS Machine Learning Services 3:00
  42. Putting them All Together 2:00
  43. Lab: Tuning a Convolutional Neural Network on EC2, Part 1 9:00
  44. Lab: Tuning a Convolutional Neural Network on EC2, Part 2 9:00
  45. Lab: Tuning a Convolutional Neural Network on EC2, Part 3 6:00
  Video Name Time
  1. Section Intro: Machine Learning Implementation and Operations 1:00
  2. SageMaker's Inner Details and Production Variants 11:00
  3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass 4:00
  4. SageMaker Security: Encryption at Rest and In Transit 5:00
  5. SageMaker Security: VPC's, IAM, Logging, and Monitoring 4:00
  6. SageMaker Resource Management: Instance Types and Spot Training 4:00
  7. SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's 5:00
  8. SageMaker Inference Pipelines 2:00
  9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1 5:00
  10. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2 11:00
  11. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3 12:00
  Video Name Time
  1. Section Intro: Wrapping Up 1:00
  2. More Preparation Resources 6:00
  3. Test-Taking Strategies, and What to Expect 10:00
  4. You Made It! 1:00
  5. Save 50% on your AWS Exam Cost! 2:00
  6. Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only 1:00

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