Practical Data Science with Amazon SageMaker

Course DateStart TimeEnd TimeTime ZoneLocationDaysPrice
Sep 2, 202209:0005:00EDTIn Class or by Video Conference1$675 USDPurchase
Oct 7, 202212:0008:00EDTIn Class or by Video Conference1$675 USDPurchase
Oct 14, 202209:0005:00EDTIn Class or by Video Conference1$675 USDPurchase
Oct 14, 202212:0008:00EDTIn Class or by Video Conference1$675 USDPurchase
Nov 18, 202209:0005:00ESTIn Class or by Video Conference1$675 USDPurchase
Dec 2, 202209:0005:00ESTIn Class or by Video Conference1$675 USDPurchase


AWS-PDSASM

Practical Data Science with Amazon SageMaker

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.

Duration: 1 Day

Prerequisites

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • AWS Technical Essentials (or equivalent experience with AWS)

Audience

  • Developers
  • Data Scientists

Learning 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

Topics

  • Introduction to Machine Learning
  • Introduction to Data Prep and SageMaker
  • Problem Formulation and Dataset Preparation
  • Data Analysis and Visualization
  • Training and Evaluating a Model
  • Types of Algorithms
  • XGBoost and SageMaker
  • Automatically Tune a Model
  • Deployment / Production Readiness
  • Relative Cost of Errors
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