Mathematics for Machine Learning and Data Science
Instructors: Luis Serrano
Also available on Coursera
Mathematics for Machine Learning and Data Science
Beginner
3 Courses
219 Video Lessons
59 Reading Lessons
23 Practices
9 Graded Assignments
Instructor: Luis Serrano
DeepLearning.AI
What you'll learn
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.
About this course
Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you’ll need basic to intermediate Python programming skills to be successful.
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 visualizations to help you see how the math behind machine learning actually works.
We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.
Instructor
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 (functions, basic algebra), a beginner’s understanding of machine learning concepts, and basic familiarity with a programming language, ideally Python (loops, functions, if/else statements, lists/dictionaries, importing libraries) will help you get the most out of this specialization.
Enroll now and take your career to the next level!
Skills you will gain
Course Slides
You can download the annotated version of the course slides below.
Learner Reviews
Frequently Asked Questions
Course Outline
60 Video Lessons • 23 Reading Lessons • 7 Practices • 3 Graded Assignments
Specialization introduction
Video • 7 mins
Course introduction
Video • 1 min
What to expect and how to succeed
Video • 1 min
A note on programming experience
Video • 1 min
Notations
Reading • 10 mins
Learning Python: Recommended Resources
Reading • 10 mins
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!
Reading • 1 min
Linear Algebra Applied I
Video • 6 mins
Linear Algebra Applied II
Video • 6 mins
Check your knowledge
Reading • 10 mins
System of sentences
Video • 5 mins
System of equations
Video • 12 mins
System of equations as lines and planes
Video • 12 mins
Interactive Tool: Graphical Representation of Linear Systems with 2 variables
Reading • 10 mins
Interactive Tool: System of Equations as Planes (3x3)
Reading • 10 mins
A geometric notion of singularity
Video • 3 mins
Singular vs non-singular matrices
Video • 4 mins
Practice Quiz 1
• 1 hour
Linear dependence and independence
Video • 7 mins
The determinant
Video • 7 mins
Practice Quiz 2
• 30 mins
Downloading your Notebook and Refreshing your Workspace
Reading • 10 mins
Introduction to NumPy Arrays
Code Example • 1 hour
Linear Systems as Matrices
Code Example • 1 hour
Graded quiz
Graded・Quiz • 2 hours
Conclusion
Video • 1 min
Week 1 - Slides
Reading • 10 mins
Mathematics for Machine Learning and Data Science
Beginner
3 Courses
219 Video Lessons
59 Reading Lessons
23 Practices
9 Graded Assignments
Instructor: Luis Serrano
DeepLearning.AI