AWS Certified Machine Learning-Specialty (ML-S)

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Course Features

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Duration

5.37 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

5.37 hours

Course Description

This course covers all aspects of Machine Learning on AWS. It prepares candidates to take the AWS Machine Learning-Specialty Certification exam (ML-S). There are four main categories: Data Engineering (Exploratory Data Analysing), Modeling, Operations, and Data Engineering. Description This video course is designed to prepare you for the AWS Machine Learning-Specialty certification exam. It lasts 7+ hours. The course uses a modular and sublesson approach with screencasting and headhsot treatments.

  • Data Engineering covers the maintenance, cleaning, and ingestion of data on AWS.
  • Exploratory Data Analysis includes topics such as data visualization, descriptive statistics and dimension reduction. It also includes information about relevant AWS services.
  • Machine Learning Modeling includes topics such as feature engineering, performance metrics and overfitting.
  • Operations includes deployment of models, A/B testing and using AI services instead of training your own model.
The supporting code for this LiveLesson is located at http://www.informit.com/store/aws-certified-machine-learning-specialty-ml-s-complete-9780135556511.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

How to operationalize machine learning models and deploy them to production on the AWS platform

How to perform data engineering tasks on AWS

How to perform machine learning modeling tasks on the AWS platform

How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome

How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS

Target Students

Data scientists who run machine learning workloads on AWS

DevOps engineers who want to understand how to operationalize ML workloads

Machine learning engineers who want to solidify their knowledge about AWS machine learning practices

Product managers who need to understand the AWS machine learning lifecycle

Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS

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