Inconsistent systems and the least squares method - First Steps in Linear Algebra for Machine
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Inconsistent systems and the least squares method - First Steps in Linear Algebra for Machine Learning
Mathematics for Data Science Specialization
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability.
This course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well.
with great assignments,one could get relation of LA with computer science.For naive english it could hurdles at first.but nice content.must for intermediatory,The material was very well presented, and the exercises were helpful for learning
In the third week, we firstly introduce coordinates in an abstract vector space. This allows us to apply the usual matrix arithmetic to abstract vectors. Next, we discuss the concept of Euclidean space which allows us to measure distances and angles in vector spaces. Then we use these measures in the least squares method to find approximate solutions of linear systems and in the linear regression model based on it. Finally, we describe the core of the most common linear classifier called Support Vector Machine.
Inconsistent systems and the least squares method - First Steps in Linear Algebra for Machine Learning
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