Welcome!

This course aims to teach you about developing AI technologies and features for personal use and for business. If you are interested in building AI and machine learning-mediated products, then this course is for you!

Program Structure

To complete this program, you must successfully complete three projects. The lesson content provided before each project will assist you in understanding the concepts and mastering the skills related to each of these projects. However, you do not need to complete all of the content or quizzes, just the projects.

You'll find the suggested project due dates for each of the projects in the program, listed in your classroom. Project due dates are suggestions, not hard deadlines. There's no penalty for submitting late projects, and you can submit a project multiple times to pass. We strongly suggest that you try to submit a final solution by the listed deadline.

Courses and Syllabus

This program is all about AI and machine learning techniques used in industry. Here is an overview of what you can expect as you move through the classroom.

There are four parts (or courses) indicated by the four portions available at the very intro of your classroom.

Introduction to AI in Business

First, we'll start by teaching you about the foundations of AI and machine learning: where is it used in industry and how do these technologies work? This section will discuss the type of data you need to create an AI product, the business cases that stand to benefit the most from AI-medicated technology, and the qualities of a good AI product team.

Creating a Dataset

In this section, you'll learn how to create your own, novel dataset using Figure Eight's data annotation platform. Data annotation is all about structuring your data such that a machine learning model can learn to automatically find patterns within that data. Here, you'll learn the best design practices for creating a dataset of your own.

Build a Model

In this section, you'll see how to build and train and end-to-end deep learning model to recognize patterns in a medical image dataset. You'll look at metrics that define the success of your trained model and parameters that affect how it trains.

Measuring Business Impact

In this final section, you'll learn how to measure the efficacy of your model after it is released. This section discusses methods for identifying bias, updating a model in response to underlying changes in the data, and end-to-end case studies that demonstrate how AI products are ever improving and evolving.

Course Projects

Intremittently, at the end of certain courses, is a project lesson. Each of these three projects must be completed to successfully complete this program. A completed project looks like the following image.

While incomplete projects will look like the following image.

Email Support

If you have a question about enrollment or payment, please email support@udacity.com.