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
Introduction to Applied Machine Learning
This course is part of Machine Learning: Algorithms in the Real World Specialization
Instructor: Anna Koop
25,283 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(737 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 6 hours
Learn at your own pace
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Assessments
5 assignments
Taught in English
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This course is part of the Machine Learning: Algorithms in the Real World Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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
Instructor
Instructor ratings
4.7 (188 ratings)Offered by
Recommended if you're interested in Machine Learning
Coursera Project Network
University of Washington
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Learner reviews
4.7
737 reviews
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Showing 3 of 737
CW
5
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.
AJ
5
Reviewed on Jun 18, 2020
Very nice , informative introduction.It's very broad and generalized introduction of the Machine Learning.
AS
5
Reviewed on Jul 11, 2021
This is course is truly amazing for the people who want to know about the things that need to be considered when making a QUAM or an ML system. Looking forward to knowing more about ML!
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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