An introduction to the linear algebra behind machine learning models

Overview

Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications.

About the Instructor

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated (Addison-Wesley, 2020), an instant #1 bestseller that has been translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University and New York University, as well as online via O’Reilly, YouTube, and the Super Data Science Podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times.

Skill Level

• Intermediate

• Appreciate the role of algebra in machine and deep learning
• Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces
• Develop a geometric intuition of what's going on beneath the hood of machine learning algorithms, including those used for deep learning
• Be able to more intimately grasp the details of machine learning papers as well as all of the other subjects that underlie ML, including calculus, statistics, and optimization algorithms
• Manipulate tensors of all dimensionalities including scalars, vectors, and matrices, in all of the leading Python tensor libraries: NumPy, TensorFlow, and PyTorch
• Reduce the dimensionality of complex spaces down to their most informative elements with techniques such as eigendecomposition (eigenvectors and eigenvalues), singular value decomposition, and principal components analysis

• Users of high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms who would now like to understand the fundamentals underlying the abstractions, enabling them to expand their capabilities
• Software developers who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
• Data scientists who would like to reinforce their understanding of the subjects at the core of their professional discipline
• Data analysts or AI enthusiasts who would like to become a data scientist or data/ML engineer and are keen to deeply understand from the ground up the field they're entering (very wise!)

• Mathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with quantitative information—such as understanding charts and rearranging simple equations—then you should be well-prepared to follow along with all of the mathematics.
• Programming: All code demos are in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.