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Gilbert Strang lectures on Linear Algebra (MIT).Linear Algebra for Machine learning by Khan Academy.Linear Algebra for Machine Learning By Applied AI Courseīest Courses To Learn Linear Algebra For Machine Learning.

Trigonometry Fundamentals by 3Blue1Brown.Essence of Linear Algebra by 3Blue1Brown.Introduction to Linear Algebra for Applied Machine Learning with Pythonīest YouTube Videos To Learn Linear Algebra For Machine Learning.Linear Algebra with the Learning Machine.Learn Algebra for Machine Learning with Math is Fun.Topics such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigen decomposition of a matrix, LU Decomposition, Symmetric Matrices, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learningīest Free Resources To Learn Linear Algebra For Machine Learningīest Websites To Learn Linear Algebra For Machine Learning What are some of the core topics you should learn in linear algebra? Linear algebra is also used in data preprocessing, data transformation, dimensionality reduction, and model evaluation. Sometimes we do clustering of input by using spectral clustering techniques, and for this, we need to know eigenvalues and eigenvectors.

For example in logistic regression, we do vector-matrix multiplication. In machine learning, most of the time we deal with scalars and vectors, and matrices. Why you should learn linear algebra for machine learning?

Want To Learn Machine Learning In An Interesting Way? If Yes, Then Check Out This Rarely Seen Free Resources: Best Resources To Learn Machine Learning.

Want To Learn About Machine Learning Algorithms With Python? If Yes, Then You Must Check Out This Post: Machine Learning Algorithms In Python.

To read the original article, click here.And If you want to learn a specific topic in Maths for Machine Learning then go through the list given below Once you’ve finished the resources above, I’d say you’re in a great place to tackle the Andrew Ng Coursera Courseor its more mature, mathematically rigorous older brother, CS 229.

Probability - Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability reviewworth checking out.

Multivariate Calculus - Again, MIT Open Courseware has good courses, and so does Khan Academy.

Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.

Linear Algebra - Professor Strang’s textbook and MIT Open Courseware courseare recommended for good reason.

Start with Mathematics for Machine Learning Specialization on Coursera. If starting from complete scratch, the topics you should certainly review/cover, in any order are as follows: