This foundational course in linear algebra introduces students to essential concepts with real-life applications in mathematics, engineering, and the sciences. Through lectures, readings, quizzes, and a project, students will master linear equations, matrix methods, and linear transformations. The course content covers topics such as systems of linear equations, row reduction, echelon form, vector equations, matrix equations, solution sets of linear systems, linear independence, and linear transformations. Additionally, students will learn to read, write, and correct mathematical proofs, becoming fluent in the language of linear algebra and gaining the ability to classify and solve linear systems of equations.
Upon completion, students will have a solid understanding of linear algebra, preparing them for further study in linear transformations. This course is the first of a three-course specialization offered by Johns Hopkins University, providing a comprehensive and practical foundation for future academic and professional pursuits in related fields.
Certificate Available ✔
Get Started / More Info
This course is divided into four modules. Module 1 covers the introduction to matrices, Module 2 focuses on vector and matrix equations, Module 3 delves into linear transformations, and Module 4 entails the final assessment.
This module introduces the basics of matrices, covering systems of linear equations, row reduction, echelon form, and matrix methods. Students will learn to manipulate matrices and solve linear systems through practical exercises and theoretical understanding.
Building upon the concepts from Module 1, this module explores vector and matrix equations, as well as solution sets of linear systems. By understanding and practicing vector and matrix equations, students will gain a deeper comprehension of linear algebra and its applications.
In this module, students will delve into the abstract concepts of linear transformations, including linear independence, the matrix of a linear transformation, and applying matrices to linear transformations. Through practical exercises, students will gain proficiency in linear transformations and related concepts.
The final assessment module provides an opportunity for students to apply their knowledge and skills acquired throughout the course. It serves as a comprehensive evaluation of their understanding of linear algebra, preparing them for further studies in this specialization.
Machine Learning for Trading is a 3-course Specialization covering quantitative trading strategies, machine learning models, and reinforcement learning techniques...
Create a Superhero Name Generator with TensorFlow in this hands-on project. Train a neural network to generate unique and creative superhero names.
Image Compression with K-Means Clustering - Learn to compress images using k-means clustering in Python with scikit-learn and build interactive GUI components in...
Learn to predict employee turnover using decision trees and random forests with scikit-learn, and create interactive Jupyter widgets for real-time model evaluation....