Mathematics for Data Science: Essential Mathematics for Machine Learning and AI

A solid foundation in mathematical knowledge is vital for the development of artificial intelligence (AI) systems. What are the key maths skills required to study machine learning and AI?

If you haven’t studied mathematics since school and have forgotten what you’ve learned, this primer course will provide you with the education you need.

What mathematics do I need to know to launch a career in artificial intelligence?

The course will cover the three main branches of mathematics used in data science and artificial intelligence: linear algebra, calculus and probability. You’ll get to learn the essential topics of each of these three areas – from equations, functions and graphs to differentiation and optimisation and vectors and matrices.

Having mastered these concepts and techniques, you’ll have the foundational knowledge to kickstart your machine learning career.

The course is ideal for anyone who wishes to learn the core mathematics techniques and concepts required to help with their career in AI, machine learning and data science.

You may be planning to study in these areas, or you may be a student looking to improve your knowledge. * Equations, Functions and Graphs * Differentiation and Optimization * Vectors and Matrices * Statistics and Probability

• Course Introduction
• About this Course
• Algebra Fundamentals
• Exponents and Polynomials
• Factorisation, Quadratic Equations and Functions
• Wrapping Up the Week
• Differential Calculus
• Differential Calculus Foundations
• Derivatives
• Differentiation
• Wrapping Up the Week
• Vectors and Matrices
• Vectors
• Matrices
• Matrix Transformations
• Wrapping Up the Week
• Statistics and Probability
• Visualising Data
• Central Tendencies
• Probability
• Confidence Intervals and Hypothesis Testing
• Wrap Up and Course Completion