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.
Enroll Now
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.
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
- Newton’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
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
“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.”