English | Size: 14.3 GB
Category: Machine Learning
6.5 Hours of Video Instruction
An introduction to the linear algebra behind machine learning models
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.
Learn How To
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
Who Should Take This Course
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.