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Machine Learning with Sagemaker (AWS)

Course content updated by LearnQuest
Price
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3 Days
DCSK-125
Classroom Training, Online Training
Limited Time Offer
20% Off Check Point Training Courses
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Valid through October 31, 2022

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Prices reflect the Accenture employee discount.
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Course Description

Overview

This Machine Learning with Sagemaker (AWS) course intended for data scientists and software engineers. The course combines an overview and understanding of Machine Learning concepts with specific implementation in SageMaker. In addition, it brings in other tools outside of SageMaker when required.

Machine Learning (ML) is the killer app for Big Data. Amazon Machine Learning brings the power of ML to a regular programmer and provides ML as a service. However, to use ML effectively, one needs to understand the models used and how to utilize them on Amazon. For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, and specific use cases.
 

Objectives

Upon completion of the Machine Learning with Sagemaker (AWS) course, students will be able to:
  • Attain thorough understanding of popular machine learning algorithms, their applicability and limitations
  • Practice the application of these methods in the Amazon machine learning environment
  • Achieve clarity in the real-world use of machine learning by illustrating each method with practical use cases

Audience

  • Data Scientists and Software Engineers

Prerequisites

    • Familiarity with programming in at least one language
    • Be able to navigate Linux command line
    • Basic knowledge of command line Linux editors (VI / nano)
    • Basic familiarity with AWS (optionally may be provided in the first day on the course)

Topics

Introductions and Overviews
  • Data ETL
    • Go into one example in detail, implemented on AWS Redshift
    • Provide pointer to other examples for self-study
  • Machine learning
    • Goals, results, supervised/unsupervised
    • Which part of ML is implemented in the Amazon Machine Learning
    • SageMaker (AWS) Overview
Supervised Learning
  • Linear regression
  • Logistic regression and multinomial logistic regression
  • SVM, decision trees, random forests, neural networks
  • Labs for every section above
Unsupervised Learning
  • K-Means
  • Other types of unsupervised learning
    • Hierarchical clustering
    • Mixture models
    • DBSCAN
Data Visualization
  • Visualization examples for the models above
  • Links to other visualizations for self-study
SageMaker
  • Intro
  • SageMaker Details
    • Using Built-in Algorithms
    • Using Your Own Algorithms
    • Using TensorFlow
    • Using Apache MXNet
    • Using Apache Spark
    • Amazon SageMaker Libraries
    • Authentication and Access Control
    • Monitoring
  • Big Data Overview

    EJBD-100
    • Duration: 1 Day
    • Delivery Format: Classroom Training, Online Training
    • Price: 700.00 USD
  • Big Data on AWS

    AWS-160
    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,025.00 USD
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