This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
Introduction to Applied Machine Learning
This course is part of Machine Learning: Algorithms in the Real World Specialization
Instructor: Anna Koop
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There are 4 modules in this course
This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
What's included
12 videos6 readings2 assignments3 discussion prompts
This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for 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!
What's included
8 videos4 readings1 assignment2 discussion prompts
This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
What's included
9 videos2 readings1 assignment2 discussion prompts
This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.
What's included
7 videos2 readings1 assignment2 discussion prompts
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Recommended if you're interested in Machine Learning
University of Illinois Urbana-Champaign
Amazon Web Services
Stanford University
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Reviewed on Jan 5, 2020
Course gives a broader understanding of any Machine Learning project, how to approach and what are the important things to keep in mind.
Reviewed on Jun 18, 2020
Very nice , informative introduction.It's very broad and generalized introduction of the Machine Learning.
Reviewed on Sep 14, 2020
The lectures are very clear and easy to follow. More importantly, it gives me a big picture of how Machine Learning can be applied to the real-world business.
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