Amazon SageMaker notebooks provide a fully-managed environment for machine learning and data science development. This playground lab allows you to choose from Amazon's curated library of sample notebooks to learn about what is most important to you. There are hundreds of notebooks to choose from. Example topics covered in the sample notebooks include:

• Machine learning basics
• Popular machine learning libraries including Tensorflow, PyTorch, Scikit-Learn, and Apache MXNet
• DeepAR forecasting of energy consumption 
• Image classification
• Reinforcement learning for portfolio optimization
• Sentiment analysis of IMDB movie reviews
• Clustering similar United States counties using population date
• SageMaker debugging
• and much more!

You may also use the playground to develop your own machine learning models and gain hands-on experience with SageMaker.

Lab Objectives

Upon completion of this Lab you will be able to:

• Use SageMaker notebook instances to learn about SageMaker and machine learning concepts
• Experiment with SageMaker notebooks

Intended Audience

This lab is intended for:

• Anyone interested in SageMaker or machine learning

Prerequisites

You should be familiar with:

• Some knowledge of machine learning concepts is beneficial, but not required
• Basic programming using Python 3. The Introduction to Python learning path is useful for meeting this requirement. 
• Basic programming using R (if you select any of the R notebooks)

Updates

February 15th, 2021 - Updated blazingtext_hosting_pretrained_fasttext comments to work with SageMaker v2 library

December 16th, 2020 - Included a warning about the rollout of the Python SageMaker v2 library potentially breaking notebooks targeting v1 until AWS removes or updates impacted notebooks