Online Courses 
FutureLearn
Moocable is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
Description
This course is designed to help you get up to speed on the key concepts and notation on which machine learning an AI are based. This course is not a full math curriculum. It’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning.
We’ll start with some basic algebra to get started with equations and functions, then we’ll dive into some differential calculus to explore derivatives and optimisation. We’ll also look at some linear algebra and cover vectors and matrices, before finally getting to grips with some statistics and probability.
On completion of this course, you will be able to:
Apply basic Mathematical principles required in Data Analytics Demonstrate your understanding of Algebra Fundamentals Demonstrate your understanding of Quadratic Equations and Functions Demonstrate understanding of Differential Calculus Foundations Demonstrate understanding of Vectors and Matrices Demonstrate understanding of Statistics and Probability
This course is designed to fill the gaps for students who missed the key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.
Individuals in the following roles will also find this course useful:
Data Analysts Programmers
Tags
Syllabus
- Course Introduction
- About this Course
- Algebra Fundamentals
- Exponents and Polynomials
- Factorisation, Quadratic Equations and Functions
- Wrapping Up the Week
- Differential Calculus
- Differential Calculus Foundations
- Differentiation and Derivatives
- Multivariate Differentiation and Integration
- Week 2 Wrap-Up
- Vectors and Matrices
- Vectors
- Matrices
- Week 3 Wrap-Up
- Statistics and Probability
- Visualising Data
- Central Tendencies
- Probability
- Confidence Intervals and Hypothesis Testing
- Course Wrap-Up
Essential Mathematics for Data Science and Machine Learning: Using R
Affiliate notice
-
TypeOnline Courses
-
ProviderFutureLearn
This course is designed to help you get up to speed on the key concepts and notation on which machine learning an AI are based. This course is not a full math curriculum. It’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning.
We’ll start with some basic algebra to get started with equations and functions, then we’ll dive into some differential calculus to explore derivatives and optimisation. We’ll also look at some linear algebra and cover vectors and matrices, before finally getting to grips with some statistics and probability.
On completion of this course, you will be able to:
Apply basic Mathematical principles required in Data Analytics Demonstrate your understanding of Algebra Fundamentals Demonstrate your understanding of Quadratic Equations and Functions Demonstrate understanding of Differential Calculus Foundations Demonstrate understanding of Vectors and Matrices Demonstrate understanding of Statistics and Probability
This course is designed to fill the gaps for students who missed the key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.
Individuals in the following roles will also find this course useful:
Data Analysts Programmers
- Course Introduction
- About this Course
- Algebra Fundamentals
- Exponents and Polynomials
- Factorisation, Quadratic Equations and Functions
- Wrapping Up the Week
- Differential Calculus
- Differential Calculus Foundations
- Differentiation and Derivatives
- Multivariate Differentiation and Integration
- Week 2 Wrap-Up
- Vectors and Matrices
- Vectors
- Matrices
- Week 3 Wrap-Up
- Statistics and Probability
- Visualising Data
- Central Tendencies
- Probability
- Confidence Intervals and Hypothesis Testing
- Course Wrap-Up
Tags
Related Courses
Vector and Matrix Algebra
Mathematics XI Part-I
Introductory Non-Linear Dynamics
คณิตศาสตร์สำหรับวิทยาศาสตร์ข้อมูล | Mathematics for Data Science
Math for Machine Learning
Ohio State Test - Mathematics Grade 7: Practice & Study Guide
NAPLAN Year 9: Test Prep & Practice
High School 101: High School Readiness
WBJEEM (West Bengal Joint Entrance Exam): Test Prep & Syllabus
CSET Math Subtest III (213): Practice & Study Guide
EQAO Grade 9 Math: Test Prep & Practice
Loading...
Saving...
Loading...