Online Course new
Math Foundation for ML

Gain a deep understanding of the core mathematical principles that power machine learning models.

4.9

808 reviews on
506 students already have enrolled
  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Skill level:

Basic

Duration:

1 hour
  • Lessons (51 hours)

CPE credits:

2
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

  • Acquire the mathematical foundation for machine learning models.
  • Understand linear algebra, calculus, gradients, probability, and optimization.
  • Grasp the mathematics behind ML model training.
  • Relate mathematical concepts to various ML algorithms.
  • Build confidence in the math that powers machine learning.

Topics & tools

geometrymatrix decompositioncalculusoptimization algorithmgradient descentmath & statisticstheory

Your instructor

Course OVERVIEW

Description

CPE Credits: 2 Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study

Mathematics is the backbone of machine learning, and to truly excel in this field, you need a strong grasp of key mathematical concepts. From understanding data to optimising complex models, math provides the foundation for machine learning algorithms. If you want to develop, understand, and fine-tune machine learning models with confidence, this course is for you.

Many people find math intimidating, but it doesn’t have to be! When taught with real-world examples and practical applications, even the most complex concepts become easy to grasp. That’s why this course focuses on intuitive, hands-on learning rather than overwhelming you with abstract theory.

The Mathematics foundation for ML course is designed to make complex topics simple, intuitive, and engaging. Instead of dry theory from textbooks or scattered tutorials online, we offer a structured, step-by-step approach with dynamic, beautifully animated lessons that bring mathematical concepts to life.

Through this course, you will:

  • Gain a solid foundation in linear algebra, calculus, probability, and optimisation—essential for ML.
  • Learn how ML models work under the hood, from matrix operations to gradients.
  • Engage with real-world examples and storytelling that make math both accessible and exciting.
  • Reinforce your learning with interactive exercises and hands-on practice.

This course is perfect for students, professionals, and anyone aspiring to break into machine learning. A basic understanding of high school math is recommended, but no advanced background is required.

By the end of this course, you’ll see math not as an obstacle but as a powerful tool that unlocks the true potential of machine learning. Plus, you’ll earn a certificate of achievement to showcase your new skills.

Ready to build your mathematical superpower? Join us today and take the first step toward mastering machine learning!

Prerequisites

  • Basic arithmetic and algebra knowledge is required

Free preview

Course Introduction

1.1 Course Introduction

4 min

Why Learn the Math Behind ML?

1.2 Why Learn the Math Behind ML?

2 min

ML Model Development Pipeline

1.3 ML Model Development Pipeline

1 min

What is Linear Algebra?

2.1 What is Linear Algebra?

3 min

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9 in 10

people walk away career-ready

with practical data and AI skills.

94%

of AI and data science graduates

successfully change

or advance their careers.

9 in 10

of our graduates landed a new AI & data job

after enrollment

ACCREDITED certificates

Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global institutions.

Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities employers value most.

  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Certificates are included with the Self-Study learning plan.

A LinkedIn profile mockup on a mobile screen showing Parker Maxwell, a Certified Data Analyst, with credentials from 365 Data Science listed under Licenses & Certification. A 365 Data Science Certificate of Achievement awarded to Parker Maxwell for completing the Data Analyst career track, featuring accreditation badges and a gold “Verified Certificate” seal.

How it WORKS

  • Lessons
  • Exercises
  • Projects
  • Practice Exams
  • AI Mock Interviews

Lessons

Learn through short, simple lessons—no prior experience in AI or data science needed.

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Exercises

Reinforce your learning with mini recaps, hands-on coding, flashcards, fill-in-the-blank activities, and other engaging exercises.

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Projects

Tackle real-world AI and data science projects—just like those faced by industry professionals every day.

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Practice Exams

Track your progress and solidify your knowledge with regular practice exams.

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AI Mock Interviews

Prep for interviews with real-world tasks, popular questions, and real-time feedback.

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Student REVIEWS

A collage of student testimonials from 365 Data Science learners, featuring profile photos, names, job titles, and quotes or video play icons, showcasing diverse backgrounds and successful career transitions into AI and data science roles.