# 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.

*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.