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Practical Data Science with Amazon SageMaker (PDSASM)

 

Who should attend

  • Developers
  • Data Scientists

Prerequisites

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning

Course Objectives

  • Prepare a dataset for training
  • Train and evaluate a Machine Learning model
  • Automatically tune a Machine Learning model
  • Prepare a Machine Learning model for production
  • Think critically about Machine Learning model results

Course Content

In this intermediate-level course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.

Online Training

Duration 1 day

Classroom Training

Duration 1 day

Schedule

English
1 hour difference
18/09/2020 Online Training Time zone: EEST
3 hours difference
13/11/2020 Online Training Time zone: CET
8 hours difference
08/06/2020 Online Training Time zone: EDT
9 hours difference
30/06/2020 Online Training Time zone: EST
13/08/2020 Online Training Time zone: EST
30/10/2020 Online Training Time zone: EST
07/12/2020 Online Training Time zone: EST
This computer icon in the schedule indicates that this date/time will be conducted as Instructor-Led Online Training.
This is a FLEX course, which is delivered both virtually and in the classroom.
Middle East
United Arab Emirates
15/11/2020 Dubai Course language: English
Saudi Arabia
15/11/2020 Riyadh Course language: English
This is a FLEX course, which is delivered both virtually and in the classroom.