About this Specialization

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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. After completing all four courses, you will have gone through 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.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Approx. 4 months to complete
Suggested 2 hours/week
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Approx. 4 months to complete
Suggested 2 hours/week
English
Subtitles: English

There are 4 Courses in this Specialization

Course1

Course 1

Introduction to Applied Machine Learning

4.7
stars
342 ratings
91 reviews
Course2

Course 2

Machine Learning Algorithms: Supervised Learning Tip to Tail

4.7
stars
125 ratings
24 reviews
Course3

Course 3

Data for Machine Learning

4.5
stars
43 ratings
10 reviews
Course4

Course 4

Optimizing Machine Learning Performance

4.6
stars
12 ratings
3 reviews

Offered by

Alberta Machine Intelligence Institute logo

Alberta Machine Intelligence Institute

Frequently Asked Questions

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • 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.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • 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.