Introduction to Applied Machine Learning MOOC

The MOOC Introduction to Applied Machine Learning has been designed for professionals who have heard of machine learning and want to use it for automation and data analysis.

This course is for professionals who want to apply machine learning to their own data analysis and automation. In finance, medicine, engineering, business or other domains, this course will introduce problem definition and data preparation in a machine learning project.

By the end of the course, learners will be able to clearly define a machine learning problem using two approaches, and will learn to survey available data resources and identify potential ML applications. They will learn to take a business need and turn it into a machine learning application, and prepare data for effective machine learning applications.

Course modules

Module 1: Introduction to the Applied Machine Learning Applications

In this module, students will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about it. You will apply this knowledge by identifying different components essential to a machine learning business solution.

Module 2: Machine Learning in the Real World 

In this module, students will learn how to translate a business need into a machine learning problem, with applied examples to understand what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!

Module 3: Learning Data

In this module, students will learn about data acquisition and understand the various sources of training data, including ethical issues.

Module 4: Machine Learning Projects

In this module, students will learn about the Machine Learning Process Lifecycle (MLPL).Understanding the definitions and components of the MLPL and analyzing the application of the MLPL in a case study.