Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.
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 plugins and visualizations to help you see how the math behind machine learning actually works.
This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.
By the end of this Specialization, you will be ready to:
- Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
- Apply common vector and matrix algebra operations like dot product, inverse, and determinants
- Express certain types of matrix operations as linear transformations
- Apply concepts of eigenvalues and eigenvectors to machine learning problems
- Optimize different types of functions commonly used in machine learning
- Perform gradient descent in neural networks with different activation and cost functions
- Describe and quantify the uncertainty inherent in predictions made by machine learning models
- Understand the properties of commonly used probability distributions in machine learning and data science
- Apply common statistical methods like MLE and MAP
- Assess the performance of machine learning models using interval estimates and margin of errors
- Apply concepts of statistical hypothesis testing
After completing this course, learners will be able to:
- Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.
- Apply common vector and matrix algebra operations like dot product, inverse, and determinants
- Express certain types of matrix operations as linear transformations
- Apply concepts of eigenvalues and eigenvectors to machine learning problems
Matrices are commonly used in machine learning and data science to represent data and its transformations. In this week, you will learn how matrices naturally arise from systems of equations and how certain matrix properties can be thought in terms of operations on system of equations.
- Form and graphically interpret 2x2 and 3x3 systems of linear equations
- Determine the number of solutions to a 2x2 and 3x3 system of linear equations
- Distinguish between singular and non-singular systems of equations
- Determine the singularity of 2x2 and 3x3 system of equations by calculating the determinant
- Machine learning motivation
- Systems of sentences
- Systems of equations
- Systems of equations as lines
- A geometric notion of singularity
- Singular vs nonsingular matrices
- Linear dependence and independence
- The determinant
- Practice Quiz: Solving systems of linear equations
- Lab: Introduction to NumPy Arrays
- Systems of equations (3×3)
- Singular vs non-singular (3×3)
- Systems of equations as planes (3×3)
- Linear dependence and independence (3×3)
- The determinant (3×3)
- Quiz: Matrices
- Lab: Solving Linear Systems: 2 variables