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.
A simplified approach to learning mathematics for AI
Learn quickly and intuitively with our innovative pedagogy in mathematics. Our courses include easy-to-follow visualizations to help you see how the math behind machine learning actually works.
Created in partnership with Luis Serrano, AI scientist, popular YouTuber, and author of Grokking Machine Learning, this course focuses on building a practical skillset that’s immediately useful.
Through approachable lessons and hands-on exercises, you'll master mathematical concepts as they relate directly to your machine learning and data science skills.
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 will help learners get the most out of this class.
Enroll now and take your career to the next level!
Concepts you will learn
- Vectors and Matrices
- Matrix product
- Linear Transformations
- Rank, Basis, and Span
- Eigenvectors and Eigenvalues
- Derivatives
- Gradients
- Optimization
- Gradient Descent
- Gradient Descent in Neural Networks
- Netwon’s Method
- Probability
- Random Variables
- Bayes Theorem
- Gaussian Distribution
- Variance and Covariance
- Sampling and Point Estimates
- Maximum Likelihood Estimation
- Bayesian Statistics
- Confidence Intervals
- Hypothesis Testing
What Learners From Previous Courses Say About DeepLearning.AI
“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.”
“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.”
“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.”