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

Looking for studying for AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01) certification training course. Our AWS Certified Machine Learning - Specialty certification video training course training course is your number one assistant. Pass with our AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01) certification training course on the first try and become a certified professional in no time.

  • 121 Students Enrolled
  • 106 Lectures
  • 9 Hours
  • 4.5 Rating
$38.49
$34.99

Curriculum For This Course

  • Introduction

    Video Name Time
    1. Course Introduction: What to Expect 6:00

  • Data Engineering

    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

  • Exploratory Data Analysis

    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

  • Modeling

    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

  • ML Implementation and Operations

    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

  • Wrapping Up

    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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