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.
This course is part of the Machine Learning: Algorithms in the Real World Specialization
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Machine Learning: Algorithms in the Real World SpecializationAlberta Machine Intelligence InstituteAbout this Course
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Course 1 of 4 in the
Intermediate Level
Approx. 7 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessFlexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 4 in the
Intermediate Level
Approx. 7 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Syllabus - What you will learn from this course
3 hours to complete
Introduction to Machine Learning Applications
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.
3 hours to complete
12 videos (Total 44 min), 6 readings, 2 quizzes
2 hours to complete
Machine Learning in the Real World
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!
2 hours to complete
8 videos (Total 34 min), 4 readings, 1 quiz
1 hour to complete
Learning Data
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.
1 hour to complete
9 videos (Total 34 min), 2 readings, 1 quiz
1 hour to complete
Machine Learning Projects
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.
1 hour to complete
7 videos (Total 35 min), 2 readings, 1 quiz
Reviews
- 5 stars74.50%
- 4 stars19.94%
- 3 stars4.55%
- 2 stars0.28%
- 1 star0.71%
TOP REVIEWS FROM INTRODUCTION TO APPLIED MACHINE LEARNING
by SLJun 21, 2020
An excellent introduction to the fascinating world of machine learning and its endless applications. Loved the emphasis on the evaluation of the business prospect of ML as well.
by SROct 8, 2020
I have been teaching Machine Learning for some time now. This course is really good and comprehensively presents the working of machine learning process in a nutshell. Very good introductory course.
by AASep 11, 2019
very comprehensive course on applied machine learning. the most interesting information in this course is business needs for ML and what it's requirement to have a good QuAM.
by RSApr 2, 2022
I loved the way the course was structured, as it gave a very good introduction. The instructor was clear and concise during lectures.
About the Machine Learning: Algorithms in the Real World Specialization
This specialization 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 specialization will set you up to define, train, and maintain a successful machine learning application.
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