Machine learning and data science are the most popular topics of research nowadays. They are applied in all the areas of engineering and sciences. Various machine learning tools provide a data-driven solution to various real-life problems. Basic knowledge of linear algebra is necessary to develop new algorithms for machine learning and data science. In this course, you will learn about the mathematical concepts related to linear algebra, which include vector spaces, subspaces, linear span, basis, and dimension. It also covers linear transformation, rank and nullity of a linear transformation, eigenvalues, eigenvectors, and diagonalization of matrices. The concepts of singular value decomposition, inner product space, and norm of vectors and matrices further enrich the course contents.
Linear Algebra Basics
Unterrichtet auf Englisch
Was Sie lernen werden
Describe the vector spaces, vector subspaces, basis, and dimension of a vector space.
Explain the linear transformations defined on vector spaces and eigenvalues and eigenvector of a matrix, symmetric and skew-symmetric matrices.
Explain diagonalizable matrices, their applications and the inner product, and the norm of vectors and matrices.
Kompetenzen, die Sie erwerben
- Kategorie: Data Science
- Kategorie: Mathematics
- Kategorie: Machine Learning (ML) Algorithms
- Kategorie: Machine Learning
- Kategorie: Linear Algebra
Wichtige Details
Zu Ihrem LinkedIn-Profil hinzufügen
8 Quizzes, 1 Aufgabe
Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.
Erwerben Sie ein Karrierezertifikat.
Fügen Sie diese Qualifikation zur Ihrem LinkedIn-Profil oder Ihrem Lebenslauf hinzu.
Teilen Sie es in den sozialen Medien und in Ihrer Leistungsbeurteilung.
In diesem Kurs gibt es 6 Module
This module provides an overview of the course content and structure. In this module, you will learn about the different course elements. In this module, you will get acquainted with your instructor and get an opportunity to introduce yourself and interact with your peers.
Das ist alles enthalten
1 Lektüre1 Diskussionsthema
In this module, you will learn about vector space and its subspace. Further, you will learn about the set of linearly dependent and independent vectors. You will also gain insight into the linear combination and linear span of a set of vectors.
Das ist alles enthalten
6 Videos2 Lektüren2 Quizzes
In this module, you will learn about the basis and dimension of a vector space. You will learn about the concept of linear transformations defined on real vector spaces. Further, you will understand that there is a matrix associated with each linear transformation for the bases. Finally, you will get an insight into the eigenvalues of a square matrix.
Das ist alles enthalten
6 Videos5 Lektüren2 Quizzes
In this module, you will learn about the eigenvectors corresponding to the eigenvalues of a matrix. You will then learn about the properties of special matrices (symmetric and skew-symmetric). Finally, you will learn about the concept of diagonalization of a matrix (eigen decomposition of a matrix) with its applications.
Das ist alles enthalten
6 Videos3 Lektüren2 Quizzes
In this module, you will learn about the spectral value decomposition and singular value decomposition of a matrix with some applications. Further, you will learn about the inner product space and norms of vectors and matrices with two useful identities—Cauchy-Schwarz inequality and Polarization identity—for machine learning algorithms.
Das ist alles enthalten
6 Videos4 Lektüren2 Quizzes
In this module, you are provided with your term-end project, instructions to complete the project, and the criteria for how your instructor will grade your submission.
Das ist alles enthalten
3 Lektüren1 Aufgabe1 Unbewertetes Labor
Dozent
von
Empfohlen, wenn Sie sich für Machine Learning interessieren
University of Minnesota
Illinois Tech
Kennesaw State University
Warum entscheiden sich Menschen für Coursera für ihre Karriere?
Neue Karrieremöglichkeiten mit Coursera Plus
Unbegrenzter Zugang zu über 7.000 erstklassigen Kursen, praktischen Projekten und Zertifikatsprogrammen, die Sie auf den Beruf vorbereiten – alles in Ihrem Abonnement enthalten
Bringen Sie Ihre Karriere mit einem Online-Abschluss voran.
Erwerben Sie einen Abschluss von erstklassigen Universitäten – 100 % online
Schließen Sie sich mehr als 3.400 Unternehmen in aller Welt an, die sich für Coursera for Business entschieden haben.
Schulen Sie Ihre Mitarbeiter*innen, um sich in der digitalen Wirtschaft zu behaupten.
Häufig gestellte Fragen
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.