Linear Algebra with Python First Steps: Essential Math for Machine Learning
Take control of your data by honing your fundamental math skills
Topic: Math, Science, Engineering
Linear algebra—numerical math dealing with vectors, matrices, and linear functions—is critical for a wide range of disciplines, including computer science, data analysis, and machine learning. From linear regression to deep learning, the concepts of linear algebra can be found in every corner of applied computer science.
Join expert Thomas Nield to learn linear algebra fundamentals and discover how they apply to data science. You’ll explore vectors and matrices (both visually and numerically), important vector/matrix operators, linear systems and matrix decomposition, and more as you use Python’s NumPy package to perform linear algebra operations.
This is the first course in a fourpart series focused on essential math topics. These courses are grouped in pairs with this natural progression:
and
What you'll learnand how you can apply it
By the end of this live, handson, online course, you’ll understand:
 The intuition behind vectors and matrices, both visually and numerically
 Important vector/matrix operators and what they mean
 Linear systems and matrix decomposition
And you’ll be able to:
 Appreciate vectors and matrices beyond just a grid of numbers, and ponder operations in a visualized way
 Construct a system of linear equations and solve its variables
 Using Python’s NumPy package to perform linear algebra operations
This training course is for you because...
 You are working towards data science proficiency, and want to understand how numerical computing works under the hood
 You want to understand linear algebra beyond contrived grids of numbers, and develop an intuition on what operators actually represent.
 You work with data and use statistical and machine learning tools that often input/output matrices.
Prerequisites
 Basic math: addition, subtraction, multiplication, division, algebra
 Basic Python: variable creation, conditional statements, functions, loops
Recommended preparation:
 Setup Python3 and NumPy
Recommended followup:
 Take Linear Regression with Python (live online training course)
 Take  Probability with Python (live online training course)
 Take Statistics and Hypothesis Testing with Python (live online training course)
About your instructor

Thomas Nield is an operations research consultant as well as a writer, conference speaker, and trainer. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on analytics, machine learning, and mathematical optimization. He’s authored two books, including Getting Started with SQL (O'Reilly) and Learning RxJava (Packt), and has written several popular articles, including “How It Feels to Learn Data Science in 2019” and “Is Deep Learning Already Hitting Its Limitations?”
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Getting Started (5 minutes)
 Presentation: Introduction and Agenda
 What is linear algebra?
 Why learn linear algebra?
Vectors, Combining, and Scaling (25 minutes)
 Presentation: What are vectors? Combining and scaling vectors
 Handson: Adding and scaling vectors in NumPy
 Presentation: Span and linear dependence
 Exercise: Adding and scaling vectors
Transforming Vectors and Matrices (25 minutes)
 Presentation: Basis vectors, matrices, the determinant
 Handson: Matrices and the determinant in NumPy
 Exercise: Transforming a vector
 Break (5 minutes)
System of Linear Equations and Inverse Matrices (15 minutes)
 Presentation: Solving systems of linear equations with inverse matrices
 Handson: Solving systems of linear equations with NumPy
 Exercise: A word problem
Dot products (20 minutes)
 Presentation: Understanding dot products, orthogonality
 Handson: Dot products with NumPy
 Exercise: Executing a dot product
Matrix Decomposition (20 minutes)
 Presentation: Matrix decomposition, Eigenvectors, and Eigenvalues
 Handson: Matrix decomposition with NumPy
 Exercise: Decomposing a matrix
Final questions and closing (5 minutes)