About Course
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
Prerequisite Courses
- Familiarity with Python programming language
- Basic understanding of Machine Learning
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Take this skills assessment to see if you have the required knowledge to skip AWS Technical Essentials, the first course in the AWS technical training path: https://www.exitcertified.com/training-resources/skills-assessments/aws-skills-assessment
Skills Gained
- 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
Who Can Benefit
- Developers
- Data Scientists
Course Details
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
Module 1: Introduction to Machine Learning
Module 2: Introduction to Data Prep and SageMaker
- Training and Test dataset defined
- Introduction to SageMaker
- Demo: SageMaker console
- Demo: Launching a Jupyter notebook
Module 3: Problem formulation and Dataset Preparation
- Business Challenge: Customer churn
- Review Customer churn dataset