IBM Introduction to Machine Learning Specialization
Learn Machine Learning through use cases. Get up to date with the theory of Machine Learning, and gain hands-on practice through projects on Machine Learning, one of the most relevant fields of modern AI.
About this Specialization
Applied Learning Project
Learners will complete projects designed to highlight analytical and Machine Learning skills. For each project, learners will produce a summary of their insights in a similar way as they would in a professional setting. This includes producing a final deliverable that would be presented to communicate insights to fellow Machine Learning practitioners, stakeholders, C-suite executives, and Chief Data Officers.
Learners are highly encouraged to compile their completed projects into an online portfolio that showcases the skills learned in this specialization.
Some related experience required.
Some related experience required.
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Frequently Asked Questions
What is the refund policy?
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.
Can I just enroll in a single course?
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.
Is financial aid available?
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.
Can I take the course for free?
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.
Is this course really 100% online? Do I need to attend any classes in person?
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.
How long does it take to complete the Specialization?
The entire specialization requires 40-45 hours of study. Each of the 4 courses requires 7-10 hours of study.
What background knowledge is necessary?
Ideally, you should have some background in Math, Stats, and computer programming, as most demonstrations, labs, and projects use Python programming language and concepts like matrix factorization, convergence, or stochastic gradient descent.This Specialization is designed specifically for scientists, software developers, and business analysts who want to round their analytical skills in Data Science, AI, and Machine Learning, but is also appropriate for anyone with a passion for data and basic Math, Statistics, and programming skills.
Do I need to take the courses in a specific order?
We recommend you to take the courses in the order presented in the specialization page, as each course builds on material presented in previous courses.
Will I earn university credit for completing the Specialization?
What will I be able to do upon completing the Specialization?
You will be able to use high-demand Machine Learning techniques in real world data sets. You will be able to derive and communicate insights from data using Exploratory Data Analysis, Supervised Learning, and Unsupervised Learning.
More questions? Visit the Learner Help Center.