- Data Science
- Artificial Intelligence (AI)
- Machine Learning
- Predictive Analytics
- Ethics Of Artificial Intelligence
- Machine learning strategy and leadership
- Machine Learning (ML) Algorithms
Machine Learning Rock Star – the End-to-End Practice Specialization
An End-to-End Guide to Leading and Launching ML. This expansive machine learning curriculum is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.
What you will learn
Lead ML: Manage or participate in the end-to-end implementation of machine learning
Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more
Greenlight ML: Forecast the effectiveness of and scope the requirements for a machine learning project and then internally sell it to gain buy-in
Regulate ML: Manage ethical pitfalls, the risks to social justice that stem from machine learning – aka AI ethics
Skills you will gain
About this Specialization
Applied Learning Project
Problem-solving challenges: Form an elevator pitch, build a predictive model by hand in Excel or Google Sheets to visualize how it improves, and more (no exercises involve the use of ML software).
This specialization includes several illuminating software demos of ML in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The learnings apply, regardless of which ML software you end up choosing to work with.
In-Depth Yet Accessible
Brought to you by a veteran industry leader who won teaching awards when he was a professor at Columbia University, this specialization stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of ML.
Like a University Course
These three courses are also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of this specialization is equivalent to one full-semester MBA or graduate-level course.
How the Specialization Works
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.
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.
There are 3 Courses in this Specialization
Frequently Asked Questions
What is the refund policy?
Can I just enroll in a single course?
Is financial aid available?
Can I take the course for free?
Is this course really 100% online? Do I need to attend any classes in person?
Will I earn university credit for completing the Specialization?
Is this specialization for data scientists or is it for non-technical, business-level learners?
How technical is this specialization and how much math is involved?
Are the learnings specific to SAS software?
Is this specialization for industry professionals or for university students?
Do I need to take the courses in a specific order?
AI ethics: Is equitable machine learning possible or will predictive models always perpetuate social injustice?
More questions? Visit the Learner Help Center.