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Essential Math for Machine Learning: R Edition

Essential Math for Machine Learning: R Edition

Learn the essential mathematical foundations for machine learning and artificial intelligence.

Essential Math for Machine Learning: R Edition Highlights

Course enrollment

Starts on

15 April 2019

Enrollment closes on
30 September 2019

  Course duration

Duration

  • Total 36 to 48 hours
  Course Fee

Fee

Free

Course enrollment

Starts on

15 April 2019

Enrollment closes on
30 September 2019

Course duration

Duration

  • Total 36 to 48 hours
Course Fee

Fee

Free

Enrollment is Closed

About this course

This course is part of the Microsoft Professional Program Certificate in Data Science.

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like "algebra" and "calculus" fill you with dread? Has it been so long since you studied math at school that you've forgotten much of what you learned in the first place?

You're not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you've learned.

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. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.

What you'll learn

  • Familiarity with Equations, Functions, and Graphs
  • Differentiation and Optimization
  • Vectors and Matrices
  • Statistics and Probability

Prerequisites

To complete this course successfully, you should have:

  • A basic knowledge of math
  • Some programming experience – R is preferred.
  • A willingness to learn through self-paced study.

Course Syllabus

  • Introduction
  • Equations, Functions, and Graphs
  • Differentiation and Optimization
  • Vectors and Matrices
  • Statistics and Probability

Note: This syllabus is preliminary and subject to change.

Meet the instructors

Graeme Malcolm

Graeme Malcolm

Senior Content Developer
Microsoft Learning Experiences

Graeme has been a trainer, consultant, and author for longer than he cares to remember, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.

Course Outline

Enrollment is Closed
Before You Start
Welcome
Preparing for the Labs
Pre-Course Survey
Getting Started with Equations
The Distributive Property
Lab: Equations
Introduction to Linear Equations
Intercepts and Slope
Lab: Linear Equations
Systems of Equations
Lab: Systems of Equations
Lesson Review
Exponentials, Radicals, and Logarithms
Lab: Exponentials
Polynomials
Polynomial Operations
Lab: Polynomials
Factorization
Factoring Squares
Lab: Factorization
Introduction to Quadratic Equations
Lab: Quadratic Equations
Functions
Lab: Functions
Lesson Review
Assessment Questions
Rates of Change
Lab: Rates of Change
Introduction to Limits
Continuity
Finding Limits
Lab: Limits
Lesson Review
Introduction to Differentiation
Differentiability
Derivative Rules and Operations
Lab: Differentiation and Derivatives
Using Derivatives to Analyze Functions
Second Order Derivatives
Optimizing Functions
Lab: Critical Points and Optimization
Multivariate Differentiation
Lab: Multivariate Differentiation
Introduction to Integration
Lab: Integration
Lesson Review
Assessment Questions
Introduction to Vectors
Vector Addition
Lab: Vectors
Vector Multiplication
Lab: Vector Multiplication
Lesson Review
Introduction to Matrices
Lab: Matrices
Matrix Multiplication
Identity Matrices
Matrix "Division"
Solving Systems of Equations with Matrices
Lab: More Matrices
Matrix Transformations
Eigenvalues and Eigenvectors
Lab: Transformations, Eigenvectors, and Eigenvalues
Lesson Review
Assessment Questions
Data
Visualizing Data
Lab: Data and Visualization
Measures of Central Tendency
Measures of Variance
Lab: Statistics Fundamentals
Comparing Data
Lab: Comparing Data
Lesson Review
Probability Basics
Conditional Probability and Dependence
Binomial Variables and Distributions
Lab: Probability
Sample and Sampling Distributions
Confidence Intervals
Lab: Sampling Distributions
Hypothesis Testing
Lab: Hypothesis Testing
Lesson Review
Assessment Questions
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

Essential Math for Machine Learning: R Edition