Professional CertificateBeginner

Mathematics for Machine Learning and Data Science

Instructor: Luis Serrano

DeepLearning.AI

What you'll learn

  • A deep understanding of what makes algorithms work, and how to tune them for custom implementation.

  • Statistical techniques that empower you to get more out of your data analysis.

  • Skills that employers desire, helping you ace machine learning interview questions and land your dream job.

About this course

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you’ll need basic to intermediate Python programming skills to be successful.

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 visualizations to help you see how the math behind machine learning actually works.

We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.

Instructor

Luis Serrano

Luis Serrano

Serrano Academy

Who should join?

This is a beginner-friendly course for anyone who wants to develop their mathematical fundamentals for a career in machine learning and data science. A high school level of mathematics (functions, basic algebra), a beginner’s understanding of machine learning concepts, and basic familiarity with a programming language, ideally Python (loops, functions, if/else statements, lists/dictionaries, importing libraries) will help you get the most out of this specialization.

Enroll now and take your career to the next level!

Course Outline

Linear Algebra for Machine Learning and Data Science

This course is part of Mathematics for Machine Learning and Data Science

Course Slides

You can download the annotated version of the course slides below.

*Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes.

Learner reviews

Frequently Asked Questions

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 on its head, 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.

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.

What background knowledge is necessary for Mathematics for Machine Learning and Data Science?

High school math (functions, basic algebra) and programming (loops, functions, if/else, lists/dictionaries, libraries, debugging) are recommended.

How long does it take to complete the Mathematics for Machine Learning and Data Science 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.

Is this a standalone course or a specialization?

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

How do I take the specialization?

You can enroll in the Mathematics for Machine Learning and Data Science specialization on Coursera. You will watch videos and complete assignments on Coursera as well.

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.

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 preview the specialization?

Yes! You can preview the course for free by accessing the entire first module at no cost. This allows you to explore the learning experience before deciding if you’d like to continue. If you want full access to all modules, assessments, and the certificate of completion, you’ll need to upgrade to the paid version.

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.