AWS Machine Learning Foundations Course

Description

AWS Academy Machine Learning Foundations introduces students to the concepts and terminology of Artificial Intelligence and machine learning. By the end of this course, students will be able to select and apply machine learning services to resolve business problems. They will also be able to label, build, train, and deploy a custom machine learning model through a guided, hands-on approach.

Student Prerequisites

To ensure success in this course, students should have:

• General IT technical knowledge
• General IT business knowledge
• Experience scripting with Python or equivalent
• A basic understanding of statistics

Module Objectives

Module 1:Welcome to AWS Academy Machine Learning Foundations

Identify course prerequisites and objectives

• Describe the various roles that require machine learning knowledge
• Identify resources for further learning

Module 2:Introducing Machine Learning

Recognize how machine learning and deep learning are part of artificial intelligence•Describe artificial intelligence and machine learning terminology•Identify how machine learning can be used to solve a business problem

• Describe the machine learning process
• List the tools available to data scientists
• Identify when to use machine learning instead of traditional software development methods

Module 3:Implementing a Machine Learning pipeline with Amazon SageMaker

Formulate a problem from a business request

• Obtain and secure data for machine learning (ML)
• Build a Jupyter Notebook using Amazon SageMaker
• Outline the process for evaluating data •Explain why data needs to be preprocessed
• Use open source tools to examine and preprocess data
• Use Amazon SageMaker to train and host an ML model
• Use cross-validation to test the performance of an ML model
• Use a hosted model for inference
• Create an Amazon SageMaker hyperparameter tuning job to optimize a model’s effectiveness

Module 4:Introducing Forecasting

Describe the business problems solved by using Amazon Forecast

• Describe the challenges of working with time series data
• List the steps that are required to create a forecast by using Amazon Forecast
• Use Amazon Forecast to make a prediction

Module 5:Introducing Computer Vision

Describe the computer visionuse cases

• Describe the AWS managed machine learning (ML) services for image and video analysis
• List the steps required to prepare a custom dataset for object detection
• Describe how Amazon SageMaker Ground Truth can be used to prepare a custom dataset
• Use Amazon Rekognition to perform facial detection

Module 6:Introducing Natural Language Processing

Describe the natural language processing (NLP) use cases that are solved by using managed Amazon ML services

• Describe the managed Amazon ML services available for NLP
• Use managed Amazon ML Services

Module 7: Course Wrap-Up