Mathematics for Machine Learning and Data Science 专项课程

• 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. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

• 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.

• I’m not good at math, is this course still for me?

  Yes! We want to break down the barriers that hold people back from advancing their math skills. In this course, we flip the traditional mathematics pedagogy for teaching math, starting with the real world use-cases and working back to theory.

  Most people who are good at math simply have more practice doing math, and through that, more comfort with the mindset needed to be successful. This course is the perfect place to start or advance those fundamental skills, and build the mindset required to be good at math.

• What areas of mathematics will I learn in this course?

  Linear algebra (matrices, vectors, and their applications)

  Calculus (particularly function optimization using differential calculus)

  Probability (common probability distributions like Binomial, Gaussian)

  Statistics (parameter estimation, hypothesis testing)

• Why is mathematics for machine learning and data science important?

  With out-of-the-box tools, it’s easier than ever to begin a career as a machine learning engineer or data scientist. But to advance deeper in your career, create efficient models, troubleshoot algorithms, and incorporate creative thinking, a deeper understanding of the mathematics behind the models is needed.

• How long does it take to complete the Specialization?

  This specialization consists of three courses. At the rate of 5 hours per week, it will take you around 4 weeks to complete Course 1, 3 weeks to complete Course 2, and 4 weeks to complete Course 3 of the Mathematics for Machine Learning and Data Science Specialization.

• What background knowledge is necessary?

  This course is designed to be beginner-friendly, though familiarity with functions, basic algebra, and Python programming will help learners get the most out of this specialization.

• Do I need to take the courses in a specific order?

  No. Most learners would benefit from taking courses one and two together, as they introduce concepts that build upon each other, but course three is independent from the other courses in this specialization.

• Will I earn university credit for completing the Specialization?

  No, this Specialization is not for college credit.

• What will I be able to do after completing Mathematics for Machine Learning and Data Science?

  With Mathematics for Machine Learning and Data Science, you will have a foundation of knowledge that will equip you to go deeper in your machine learning and data science career.

• Is this a standalone course or a specialization?

  The Mathematics for Machine Learning and Data Science Specialization is made up of three courses.

• Do I need to take the courses in a specific order?

  No! Most learners would benefit from taking courses one and two together, as they introduce concepts that build upon each other, but course three is independent from the other courses in this specialization.

• Can I apply for financial aid?

  Yes, Coursera provides financial aid to learners who cannot afford the fee.

• How much does the specialization cost?

  A Coursera subscription costs $49 / month.

• Can I apply for financial aid?

  Yes, Coursera provides financial aid to learners who cannot afford the fee.

• Can I audit the specialization?

  You can audit the courses in the Mathematics for Machine Learning and Data Science for free.  Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

• Will I receive a certificate at the end of the specialization?

  You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.

  If you complete all 4 courses in the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.

• I want to purchase this specialization for my employees. How can I do that?

  Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

• How do I get a receipt to get this reimbursed by my employer?

  Go to your Coursera account. 

  Click on My Purchases and find the relevant course or Specialization.

  Click Email Receipt and wait up to 24 hours to receive the receipt. 

  You can read more about it here.

• Are all courses of this specialization ready for enrollment?

  The final course on probability and statistics is still getting the finishing touches, and will be ready for enrollment at the recommended learning pace of 4 weeks for Course 1 and 3 weeks for Course 2.

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