Master the Mathematics Behind AI and Unlock Your Potential
Mathematics for Machine Learning and Data Science is a beginner-friendly specialization where you’ll master the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
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What you’ll get from this course
- 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.
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!
Enroll NowConcepts you will learn
- Data Analysis
- Calculus
- Bayesian Statistics
- Linear Regression
- Machine Learning
- Probability
- Mathematics
Course Slides
Download the course slides for the Mathematics For Machine Learning & Data Science Specialization. A specialization that teaches you the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
What Learners From Previous Courses Say About DeepLearning.AI
“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
“As a Behavioral Scientist, I was able to adopt methods to understand my customers better, overcome the traditional ‘one-size-fits-all’ approach, and design interventions which account for personality and individual differences.”
“I gained confidence in my knowledge of machine learning. Since then, I’ve become a machine learning mentor, got a research paper published in IEEE, decided to pursue my Masters in Machine Learning, and was able to land a job at JP Morgan Chase.”
“The Machine Learning course became a guiding light. Andrew Ng explains concepts with simple visualizations and plots. I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company.”
