Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

4.5

stars

269 ratings

Luis Serrano +3 more instructors

15,765 already enrolled

Offered By

Mathematics for Machine Learning and Data Science SpecializationDeepLearning.AI

What you will learn

A deep understanding of the math that makes machine learning algorithms work.
Statistical techniques that empower you to get more out of your data analysis.
Fundamental skills that employers desire, helping you ace machine learning interview questions and land your dream job.
Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence

Skills you will gain

About this Specialization

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Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.

Applied Learning Project

By the end of this Specialization, you will be ready to:

Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Express certain types of matrix operations as linear transformations
Apply concepts of eigenvalues and eigenvectors to machine learning problems
Optimize different types of functions commonly used in machine learning
Perform gradient descent in neural networks with different activation and cost functions
Describe and quantify the uncertainty inherent in predictions made by machine learning models
Understand the properties of commonly used probability distributions in machine learning and data science
Apply common statistical methods like MLE and MAP
Assess the performance of machine learning models using interval estimates and margin of errors
Apply concepts of statistical hypothesis testing

Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Approximately 3 months to complete

Suggested pace of 5 hours/week

English

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Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Approximately 3 months to complete

Suggested pace of 5 hours/week

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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.

There are 3 Courses in this Specialization

Course1

Course 1

Linear Algebra for Machine Learning and Data Science

4.5

stars

233 ratings

After completing this course, learners will be able to:

• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc. • Apply common vector and matrix algebra operations like dot product, inverse, and determinants • Express certain types of matrix operations as linear transformations • Apply concepts of eigenvalues and eigenvectors to machine learning problems Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career. This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2 of this specialization, Calculus for Machine Learning and Data Science now, and Course 3, Probability and Statistics for Machine Learning and Data Science when it is released in April.

Course2

Course 2

Calculus for Machine Learning and Data Science

4.8

stars

74 ratings

After completing this course, learners will be able to:

• Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients • Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods • Visually interpret differentiation of different types of functions commonly used in machine learning • Perform gradient descent in neural networks with different activation and cost functions Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career. This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.

Course3

Course 3

Probability & Statistics for Machine Learning & Data Science

After completing this course, learners will be able to:

• Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions • Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems • Assess the performance of machine learning models using interval estimates and margin of errors • Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills. This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

Instructors

Offered by

DeepLearning.AI

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. 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 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.

More questions? Visit the Learner Help Center.

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Mathematics for Machine Learning and Data Science Specialization

What you will learn

Skills you will gain

About this Specialization

Applied Learning Project