About this Specialization
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100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

We recommend a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.

Approx. 1 month to complete

Suggested 15 hours/week

English

Subtitles: English

What you will learn

  • Check

    Clearly define an ML problem

  • Check

    Survey available data resources and identify potential ML applications

  • Check

    Prepare data for effective ML applications

  • Check

    Take a business need and turn it into a machine learning application

Skills you will gain

Applied Machine LearningMachine LearningClassification AlgorithmsMachine Learning (ML) AlgorithmsProject Management

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

We recommend a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.

Approx. 1 month to complete

Suggested 15 hours/week

English

Subtitles: English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 4 Courses in this Specialization

Course1

Introduction to Applied Machine Learning

4.6
43 ratings
14 reviews
Course2

Machine Learning Algorithms: Supervised Learning Tip to Tail

Course3

Optimizing Machine Learning Model Performance

Course4

Data for Machine Learning

Instructor

Avatar

Anna Koop

Senior Scientific Advisor
Alberta Machine Intelligence Institute, University of Alberta

About 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 research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • It is recommended that you take 4-6 months to complete this specialization.

  • We recommend a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.

  • We recommend taking the courses in sequential order.

  • You will earn a specialization certificate from Coursera however you will not receive any University of Alberta credits.

  • By the end of the specialization, you will be able to understand and manage the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.

More questions? Visit the Learner Help Center.