Zurück zu Mathematics for Machine Learning: Linear Algebra

Sterne

11,371 Bewertungen

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....

EC

9. Sep. 2019

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

PL

25. Aug. 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

Filtern nach:

von Rui_Lian

•2. Nov. 2022

Many thanks for David and Samuel! I've been struggling with linear algebra for quite a long time. I can do the math, but I get lost when I try to use linear algebra to understand something in statistics and machine learning. The intuition based approach is perfect. I like the apple-banana example, I like the transformation and visualization of eigenvector in 2 dimension. Also, the page rank case is quite cool and thought provoking.

I think I will stay on this series for following two courses.

Thanks again!

von Wayne C

•29. März 2019

Best presentation of fundamental Linear Algebra I have ever seen, hands down. (I'm an old-timer, reviewing this material to get up to speed on Machine Learning and Data Science.) While teaching the mechanics, the concepts behind them are always reinforced. Thank you for presenting this material in such a meaningful and digestible way. I also greatly appreciate the reverse-transparent-whiteboard which to me is highly preferable to the other methods I have experienced in online courseware.

von Xiaojun Y

•8. Okt. 2018

This is such a wonderful course. Two instructors explains complex concept with clarity and enthusiasm. They explained linear algebra from a different perspective. When I learned in college, I was taught to remember lots of definitions and concepts, but in this course, they teach you why we do certain steps not just how to do. However, I want to remind people who are interested in this course, it is not for beginner or who wants to learn linear algebra, instead of linear algebra for ML.

von Jonathan F

•20. Mai 2018

Excellent introduction. For me, as someone who had studied vectors and matrices at school, decades ago, it was wonderful to go back and re-learn this stuff in a different way. This course is much more focused on the meaning and usefulness of these things, rather than just learning how to do the maths. The first 3 minutes of the session on eigenvectors brilliantly showed in graphical form what they really are, something I'd never really grasped at school. Recommended.

von Raymond I M J

•2. Feb. 2020

An excellent breakdown of linear algebra and the tools and processes that it takes to perform these operations. The lectures give you a good understanding of the concepts of vectors, scalars, dot product, matrices, and eigenvalues and vectors. I would highly recommend this course for anyone who is needing to understand how linear algebra can be conducted via computers, while still grasping the underlying concepts that make one proficient at linear algebra.

von zachary k

•10. Mai 2020

I had previously taken linear algebra, but this was a good refresher. The pace of this course is quite fast for 5 weeks, and the course does not dive into any proofs. It may be useful to get some outside supplements to get through the materials. I really enjoyed the way that the concepts were explained and presented such as eignvalues/vectors. They help provide some intuition instead of simply presenting the formula or grinding through proofs.

von Nelson F A

•25. Apr. 2019

This is a great course! Be advised: It is very challenging and will kick your butt if you haven't seen much linear algebra before. The content in the course won't always be enough to solve all of the assignments. But look into the forums and use some other sources and you will succeed in this course. Overall I am glad I took it even if it will take a little longer until I can say that I master everything that was covered in the course.

von Sébastien W

•22. Juni 2019

The perfect dosage of the key elements in linear algebra to mastering the concepts of machine learning. The course leaves you with a clear intuition for vectors and matrices and how these objects can be manipulated, and most importantly why these objects are fantastic. I am an immunologist with a little background in machine learning and my last studies in mathematics taken 15 years ago, but this course has the perfect level I need.

von Burouj A

•6. Juli 2019

This course was like God-gifted.

I had just finished my 2nd sem at college(BTech) and we had Matrices in the syllabus so I knew how to calculate (just calculate -_-) eigenvalues, vectors and so on but I just saw them as numbers. At my college, we were not given such geometric insight and when I learned it through this course, MY GOD was I blown away.

I feel so lucky to have found this course! I learned A TON of stuff.

Thanks!!

von Luka

•16. Mai 2020

I enjoy attending this course. I consider this course really good, mostly due to a lot of intuitive examples about particular subjects of study, explanations that were clear and enthusiastic professors. Finishing this course gave me motivation to learn more about machine learning and mathematics that it's based upon.

von Karandeep

•9. Okt. 2020

This course is great for those who want to understand the geometric meaning of linear algebra. Really loved the course videos and quizzes. Just one suggestion - Coding assignments should be bit more challenging as this course is targeted around ML, maybe some small Kaggle like project at the end of course.

von Siddhant J

•13. Apr. 2020

Excellent, crisp and to the point. Instructors made the concepts way to easy to understand. Enjoyed my time learning from them and ofcourse relevant material was provided.

von Michael P

•27. Juni 2021

I think Professor David Dye's Linear Algebra video is the best course. It's much more clear, intuitive, and focused in the machine learning domain. I like it so much!!!

von Ankur A

•2. Feb. 2023

Good course. You need to know Matrices, some Algebra, vectors, Python and Maths beforehand to learn in this course, else you will end up frustrated and annoyed.

von David S

•1. Jan. 2021

A good value, well organized, with many exercises for practice. Effectively uses visuals, and contains the occasional very creative example.

Some caveats

a) this course is not for the absolute beginner. You'll need secondary / high school math, and basic familiarity with python

b) understanding linear algebra at this level is a second year full semester course at university. So if you want to understand the concepts - rather than just get the certificate - be prepared to use outside resources and invest considerably more time than advertised. Some linear algebra topics are skipped (cross product), and others are not well integrated into the course (Einstein summation)

c) while linear algebra is central to understanding machine learning, there are very few machine learning applications in this course.

And finally a small annoyance: I wish the instructors would get out of the way of the whiteboard at the end, so I could get a screen capture.

Overall, a worthwhile course.

D

von 马镓浚

•26. Aug. 2022

Overall a good course. But I think in order to get the most of this course, it would be better that you already had some knowledge of linear algebra. Then this course could give you some intuition. Put another way, this course is not for complete beginners in linear algebra. And the programming assignments are also good. Highly recommended!

von khaled W S

•25. März 2019

totally enjoyed it. requires a bit of side research as any online course would. some of the quizzes were not directly related to the video that preceded them as one would expect. However, a fun course and covers a lot of important basics for it's relatively short duration.

von JUNXIANG Z

•17. Mai 2019

This course reviews the essential concept of linear algebra in the context of machine learning. However, it would be much better if it provided more optional exercise and reading materials.

von Ralph T

•4. Mai 2019

decent course. It gives a good enough background to understand the mathematics necessities of many areas of data science. could be more thorough and dive deeper into some of the content.

von Mark J T

•2. Aug. 2019

Good course because it shows how to understand geometrically, things that I had hitherto only understood computationally.

von Philip A

•16. Mai 2019

Excellent Instruction

von Neel K

•10. Mai 2020

For the most part, I enjoyed this course. Most of the math explained is fairly easy to understand. They cover the fundamentals of linear algebra, and provide plenty of assignments and practice exercises to test your knowledge. However, some of the video explanations are extremely confusing and feel rushed. For example, some videos in Week 4 and 5 like Reflecting in a plane using Gram-Schmidt and the PageRank algorithm were so hard to understand that I had to learn about them from elsewhere on the internet (I used MIT OCW a lot). This isn't very convenient, especially if you're paying for the course. Furthermore, I felt like more videos explaining the applications of linear algebra in machine learning could've been made, and the ones that were already made could've been made in more detail (for example, the term 'span' was never formally explained). Lastly, I would've loved it if there was another week dedicated solely to introduce the coding bit, because it's really difficult and takes a while if you have little or no prior experience in python. All in all though, I enjoyed this course, and I would recommend trying to complete both Linear Algebra and Multivariate Calculus in one month, because it's not worth paying more than that.

von maytat l

•20. Nov. 2019

Challenging course. Much more difficult that I expected. It took me 7-9 hours a week. The overall course material itself was good building-blocks to further understand application of machine learning. However, explanation in some topics should have more detailed explanation and examples to further understand the concept. There were many times, I need to re-watch each video over and over again, paused it, and figured things out on my own. The programming assignments were the most challenging task. I just began to learn Python and found it very difficult because there were so many codes I haven't learnt before. I think for those who has not learnt Python at all may find really really difficult to pass the assignments.

von Gabrial D

•7. Juni 2022

I paid, started the course, felt I could not understand the concepts and discontinued. Later after a while, happened to watch 3B1B Linear Algebra videos, again paid, started the course, could able to finish. I think concepts are explained better there, with great animations.

von Peter B H

•26. Nov. 2019

The content was good, but a couple of times what was said didn't gel with what was being drawn/written/done. Since I'm learning, this took me longer to double check when I misunderstood something whether it was the concept or a mistake in the delivery.

- Google Data Analyst
- Google-Berufszertifikat Digitales Marketing und E-Commerce
- Google-Berufszertifikat IT-Automatisierung mit Python
- Google IT-Support
- Google-Projektmanagement
- Google-UX-Design
- Vorbereitung auf die Google Cloud-Zertifizierung: Cloud Architect
- IBM Cybersecurity Analyst
- IBM Data Analyst
- IBM Data Engineering
- IBM Datenverarbeitung
- IBM Full Stack-Cloudentwickler
- IBM Machine Learning
- Buchhaltung mit Intuit
- Meta Front-End-Entwickler
- Berufszertifikat DeepLearning.AI TensorFlow Developer
- Berufszertifikat SAS-Programmierer
- Eine Karriere starten
- Auf eine Zertifizierung vorbereiten
- Bringen Sie Ihre Karriere voran
- So entdecken Sie Syntaxfehler in Python
- So finden Sie Ausnahmen in Python
- Alle Programmier-Tutorials anzeigen

- Kostenlose Kurse
- Kurse zu künstlicher Intelligenz
- Blockchain-Kurse
- Informatikkurse
- Gratiskurse
- Cybersicherheitskurse
- Datenanalyse-Kurse
- Datenverarbeitungskurse
- Englischsprachige Kurse
- Full-Stack-Webentwicklungskurse
- Google-Kurse
- Personalwesen-Kurse
- IT-Kurse
- Englisch-Sprachkurse
- Microsoft-Excel-Kurse
- Produktmanagement-Kurse
- Projektmanagement-Kurse
- Python-Kurse
- SQL-Kurse
- Agile-Zertifikate
- CAPM-Zertifizierung
- CompTIA A+-Zertifizierung
- Datenanalyse-Zertifizierungen
- Scrum-Master-Zertifizierungen
- Alle Kurse anzeigen

- Kostenlose Online-Kurse, die Sie an einem Tag absolvieren können
- Beliebte kostenlose Kurse
- Wirtschaftsjobs
- Cybersicherheitsjobs
- Einstiegsjobs in der IT
- Fragen im Vorstellungsgespräch für Datenanalysten
- Datenanalyse-Projekte
- So werden Sie Datenanalyst
- So werden Sie Projektmanager
- IT-Kompetenzen
- Fragen im Vorstellungsgespräch für Projektmanager
- Python-Programmierkenntnisse
- Stärken und Schwächen im Vorstellungsgespräch
- Was macht ein Datenanalyst
- Was macht ein Software-Ingenieur
- Was ist ein Dateningenieur
- Was ist ein Datenwissenschaftler
- Was ist ein Produktdesigner
- Was ist ein Scrum-Master
- Was ist ein UX-Forscher
- So erwerben Sie eine PMP-Zertifizierung
- PMI-Zertifizierungen
- Beliebte Zertifizierungen für Cybersicherheit
- Beliebte SQL-Zertifizierungen
- Alle Coursera-Artikel lesen

- Google-Berufszertifikate
- Zertifikate über berufliche Qualifikation
- Alle Zertifikate anzeigen
- Bachelorabschlüsse
- Masterabschlüsse
- Abschlüsse in Informatik
- Abschlüsse in Data Science
- MBA- und Business-Abschlüsse
- Abschlüsse in Datenanalyse
- Abschlüsse im Gesundheitswesen
- Abschlüsse in Sozialwissenschaften
- Management-Abschlüsse
- Vergleich BA-/BS-Abschluss
- Was ist ein Bachelorabschluss?
- 11 gute Lerngewohnheiten
- So verfassen Sie ein Empfehlungsschreiben
- 10 gefragte Jobs, die Ihnen mit einem Wirtschaftsabschluss offenstehen
- Lohnt sich ein Master in Informatik?
- Alle Studiengänge anzeigen
- Coursera Indien
- Coursera Großbritannien
- Coursera Mexiko