This program is all about advanced machine learning techniques used in the industry. Here is an overview of what you can expect as you move through the classroom.
The content is divided into modules listed below
Welcome to the Nanodegree.
Software Engineering Fundamentals
Machine Learning in Production
Machine Learning Deployment Case Studies
Build Your Own Machine Learning Portfolio (Capstone) Project.
First, we'll start by teaching you some best practices for software engineering. You'll learn how to optimize your code, write tests and documentation for a code base, and build a Python package of your own. These skills are valuable in any engineering job and will act as a great foundation for applying machine learning skills in industry.
In this section, you'll learn about cloud services and model deployment. Deployment means making a model available for use in a piece of hardware or web application, such as a voice assistant or recommendation engine. In this lesson, you'll see how to analyze housing data and deploy a predictive model in SageMaker. In addition to learning about model deployment, you’ll also learn about model serving and updating. You'll learn how to connect a deployed model to a website through an API using AWS services. After deploying the model, you’ll update the model to account for changes in the underlying text data—an especially valuable skill in industries that continuously collect data. By the end of this section, you should have all the skills you need to train and deploy models to solve tasks of your own design!
In partnership with AWS, we’ve created a course that examines a variety of machine learning models as they are applied, at-scale, to real-world tasks. You’ll learn how to deploy both unsupervised and supervised algorithms and apply them to tasks such as feature engineering and time series forecasting. This content addresses questions such as:
How do you decide on the correct machine learning model for a given task?
How can you utilize cloud deployment tools such as AWS SageMaker to work with data and improve your machine learning models?
Examples of unsupervised and time-series, machine learning models.
This section has two phases. The first is the Capstone Proposal, during which you will draft a proposal outlining the domain of the problem you would like to solve, and your approach. This is followed by the Capstone Project: here, you will leverage your newly-learned skills to solve the problem—as outlined in your proposal—by applying machine learning algorithms and techniques.