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2.3 Learn data storage concepts
2.4 Learn ETL solutions (Extract-Transform-Load)
2.5 Understand data batch vs data streaming
2.3 Learn data storage concepts
2.4 Learn ETL solutions (Extract-Transform-Load)
2.5 Understand data batch vs data streaming

AWS Certified Machine Learning-Specialty ML

By Noah Gift
      1. AWS Certified Machine Learning-Specialty (ML-S): Introduction
        2m 5s
      1. Learning objectives
        1m 13s
      2. 1.1 Get an overview of the certification
        5m 56s
      3. 1.2 Use exam study resources
        2m 56s
      4. 1.3 Review the exam guide
        10m 45s
      5. 1.4 Learn the exam strategy
        2m 47s
      6. 1.5 Learn the best practices of ML on AWS
        3m 33s
      7. 1.6 Learn the techniques to accelerate hands-on practice
        2m 55s
      8. 1.7 Understand important ML related services
        22m 44s
      1. Learning objectives
        1m 10s
      2. 2.1 Learn data ingestion concepts
        23m 15s
      3. 2.2 Using data cleaning and preparation
        5m 37s
      4. 2.3 Learn data storage concepts
        7m 21s
      5. Taken from
        AWS Certified Machine Learning-Specialty (ML-S)
        By
        Noah Gift
        Published by
        Pearson IT Certification, 2019
      6. 2.5 Understand data batch vs data streaming
        3m 36s
      7. 2.6 Understand data security
        5m 12s
      8. 2.7 Learn data backup and recovery concepts
        6m 45s
      1. Learning objectives
        57s
      2. 3.1 Understand data visualization: Overview
        7m 20s
      3. 3.2 Learn Clustering
        5m 11s
      4. 3.3 Use Summary Statistics
        3m 36s
      5. 3.4 Implement Heatmap
        1m 44s
      6. 3.5 Understand Principal Component Analysis (PCA)
        2m 57s
      7. 3.6 Understand data distributions
        3m 19s
      8. 3.7 Use data normalization techniques
        3m 37s
      1. Learning objectives
        49s
      2. 4.1 Understand AWS ML Systems: Overview (Sagemaker, AWS ML, EMR, MXNet)
        16m 26s
      3. 4.2 Use Feature Engineering
        10m 43s
      4. 4.3 Train a Model
        3m 45s
      5. 4.4 Evaluate a Model
        5m 4s
      6. 4.5 Tune a Model
        2m 45s
      7. 4.6 Understand ML Inference
        5m 2s
      8. 4.7 Understand Deep Learning on AWS
        9m 36s
      1. Learning objectives
        1m 6s
      2. 5.1 Understand ML operations: Overview
        6m 7s
      3. 5.2 Use Containerization with Machine Learning and Deep Learning
        7m 32s
      4. 5.3 Implement continuous deployment and delivery for Machine Learning
        5m 45s
      5. 5.4 Understand A/B Testing production deployment
        2m 38s
      6. 5.5 Troubleshoot production deployment
        5m 21s
      7. 5.6 Understand production security
        6m 43s
      8. 5.7 Understand cost and efficiency of ML systems
        7m 24s
      1. Learning objectives
        50s
      2. 6.1 Create Machine Learning Data Pipeline
        9m 3s
      3. 6.2 Perform Exploratory Data Analysis using AWS Sagemaker
        6m 32s
      4. 6.3 Create Machine Learning Model using AWS Sagemaker
        7m 20s
      5. 6.4 Deploy Machine Learning Model using AWS Sagemaker
        9m 16s
      1. Learning objectives
        55s
      2. 7.1 Sagemaker Features
        25m 27s
      3. 7.2 DeepLense Features
        10m 49s
      4. 7.3 Kinesis Features
        6m 47s
      5. 7.4 AWS Flavored Python
        5m 15s
      6. 7.5 Cloud9
        8m 41s
      1. AWS Certified Machine Learning-Specialty (ML-S): Summary
        40s