AWS Certified Machine Learning: Machine Learning in SageMaker

Amazon Web Services    |    Intermediate
  • 12 videos | 1h 27m 8s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 134 users Rating 4.3 of 134 users (134)
Amazon SageMaker provides broad-set capabilities for machine learning (ML) as it helps to prepare, train, and quickly deploy ML models. Use this course to learn more about the basic capabilities of SageMaker and work with it to implement solutions to various machine learning problems. Explore features and functionalities of SageMaker through practical demos and discover how to implement hyperparameter tuning. This course will also help you explore algorithms in SageMaker, such as linear learner, XGBoost, object detection, and semantic segmentation. After completing this course, you'll be able to train and tune a range of algorithms in order to solve simple classification tasks for natural language processing (NLP) and computer vision.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe the features and capabilities of amazon sagemaker
    Work with the basic features of amazon sagemaker
    Work with common sagemaker studio tasks, such as clone a git repository, upload files, and stop training jobs
    Build machine learning (ml) solutions by selecting existing resources and launch them with a single click in sagemaker studio
    Outline how to use linear learner and xgboost (extreme gradient boosting) for classification and regression problems
  • Build and train an image classification model in sagemaker
    Describe how object detection algorithms built on top of vgg and resnet work to predict the objects present in the image and their confident score
    Recognize the use of sagemaker’s semantic segmentation algorithm to predict the class of each pixel in an image and get shapes of objects
    Describe hyperparameter tuning jobs in sagemaker and name recommended practices
    Create a sagemaker notebook to train and finetune an object detection algorithm
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 9s
  • 7m 5s
    During this video, you will discover the features and capabilities of Amazon SageMaker. FREE ACCESS
  • Locked
    3.  Getting Started with SageMaker Studio
    5m 21s
    In this video, you will learn how to work with the basic features of Amazon SageMaker. FREE ACCESS
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    4.  Working with Common Tasks in SageMaker Studio
    8m 48s
    Discover how to work with common SageMaker Studio tasks, such as cloning a git repository, uploading files, and stopping training jobs. FREE ACCESS
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    5.  Building ML Solution with SageMaker JumpStart
    8m 33s
    In this video, you will build machine learning (ML) solutions by selecting existing resources and launching them with a single click in SageMaker Studio. FREE ACCESS
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    6.  Linear Learner & XGBoost in SageMaker
    11m 14s
    After completing this video, you will be able to outline how to use Linear Learner and XGBoost (eXtreme Gradient Boosting) for classification and regression problems. FREE ACCESS
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    7.  Classifying Images with SageMaker
    17m 8s
    In this video, you will learn how to build and train an image classification model in SageMaker. FREE ACCESS
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    8.  Object Detection with SageMaker
    5m 36s
    Upon completion of this video, you will be able to describe how object detection algorithms work to predict the objects present in the image and their confident score. FREE ACCESS
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    9.  Semantic Segmentation with SageMaker
    5m 22s
    During this video, you will learn how to recognize the use of SageMaker's semantic segmentation algorithm to predict the class of each pixel in an image and get shapes of objects. FREE ACCESS
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    10.  How to Perform Model Tuning with SageMaker
    7m 6s
    Find out how to describe hyperparameter tuning jobs in SageMaker and name recommended practices. FREE ACCESS
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    11.  Performing Model Tuning and Training with SageMaker
    7m 46s
    Learn how to create a SageMaker notebook to train and fine-tune an object detection algorithm. FREE ACCESS
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    12.  Course Summary
    1m
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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