I found the Linear Algebra for Machine Learning and Data Science course to be an excellent resource for improving my understanding of linear algebra. The course covers a wide range of topics, including matrices, vectors, and eigenvalues, and the material is presented in a clear and concise manner.

One of the only downsides of the course was the lab sections, which were challenging to follow without any accompanying lectures. However, with enough time and effort, I was able to work through the labs and gain a deeper understanding of the course material.

Overall, I highly recommend this course to anyone looking to improve their understanding of linear algebra for machine learning and data science. Thank you to the course creators for putting together such an excellent resource.

This course is exceptionally suited for beginners starting their Data Science journey. It provides a comprehensive introduction to Linear Algebra, presented in a highly accessible manner. The instructor's teaching style is exceptional, offering clear explanations and maintaining engaging content. Furthermore, the use of effective visualization tools greatly enhances the understanding of the concepts.

What truly sets this course apart is the practicality of the assignments. DeepLearning.AI impressively bridges the gap between basic mathematics and real-world applications, fostering intuition and interest among aspiring learners entering this initially daunting domain. The practical approach makes the course truly amazing and highly recommended.

The "Linear Algebra for Machine Learning and Data Science" course by DeepLearning.AI, taught by Luis Serrano, is an exceptional resource for anyone looking to strengthen their mathematical foundation in AI and data science. The course provides a clear and practical approach to essential concepts like linear algebra, matrix operations, and Principal Component Analysis (PCA). The hands-on Python exercises, combined with insightful visualizations, make complex topics accessible and engaging. The course effectively bridges theory and practice, enhancing both understanding and application of mathematical principles. Highly recommended for anyone serious about mastering the math behind machine learning!

It was fantastic! The course was well-structured and covered many, complex topics in a clear way. The instructor was very engaging, making complex concepts easy to understand. The best part was using visuals to explain the theories and practical examples, I wish all courses would use such visuals! I really enjoyed the practical examples and coding exercises. Especially as the code was explained. I highly recommend this course to anyone looking to improve their understanding of linear algebra for machine learning. I am looking forward to completing any other DeepLearningAI course.

Through several online platforms, I've learned about LA to varying degrees in the past, including: MIT opencourseware course taught by professor Gilbert Strang, YouTube channel 3Blue1Brown, and WolframAlpha. Each has provided a different learning perspective, all very useful. All of that background knowledge was expanded on, refreshed, consolidated and applied to the field of ML in a very practical and hands-on way in this course. Thank you to the folks at DeepLearning.AI for making this content available to all.

Great course!. Explanations go from a more general or intuitive perspective to the mathematical concept, which in turn is explained through different ways such as geometrically (graphically) to do operations on equations, matrices, vectors,.. Week after week is presented the machine learning context and the reason why that linear algebra concept is important in any particular application. I really enjoyed also the real examples from the quizes that give even more context and a better understanding of the topics.

I have struggled with Linear Algebra courses in the past but I have never struggled as badly with any parts of this course. I am also incredibly glad I choose to do this course because towards the end they linked back linear algebra to actual machine learning techniques. I would have had a hard time linking linear algebra back to machine learning if they didn't show me in this course. I would highly reccomend this course to anybody looking to learn linear algebra to help them start their machine learning career!

I really like the simplicity of the video and visual cues. It really helps us to learn. On thing I need to constructively criticize is the topic of eigenvalue and eigenvector. I believe more example is needed and more detail on how to calculate eigenvectors itself. The question in the quiz is the 3x3 matrix with repeated eigenvalue, which is not really explained. I have to resort to other youtube video just to understand the techniques to solve it. Other that this, this is really good course

I recently completed this course on Coursera and I am truly impressed. Professor Luis Serrano has a remarkable ability to simplify complex topics, making learning enjoyable and engaging. I highly recommend this course to anyone looking to strengthen their mathematical foundation in AI and machine learning. Additionally, Coursera's AI tools enhance the learning experience by providing immediate feedback, making it an invaluable resource for learners.

This course strikes the perfect balance between theory and practical application. The theoretical concepts are explained in depth, but there are also plenty of examples and exercises to help you understand how to use linear algebra in real-world scenarios. Whether you're a student studying math or someone looking to apply linear algebra to fields like computer science or engineering, this course is highly recommended

The course content was comprehensive and exceptionally well-structured, covering a wide range of fundamental concepts and techniques in linear algebra. The clear explanations, engaging examples, and interactive exercises made even complex topics accessible and enjoyable to learn. I truly appreciated the thoughtful progression of the course, starting from the basics and gradually building up to more advanced concepts.

A beneficial course for beginners. These concepts are covered in school years but this course is an excellent source to recover what one might have forgotten. The course starts slow and speeds up later on. I prefer to cover the starter basics at a faster pace and spend more time and lectures on the latter part which is more complicated.

I can't wait to finish the second module.

I loved the course. A little background in algebra itself, python programming and similar topic is necessary, otherwise you may be overwhelmed by some new topics you encounter. The assignment felt like "real" assignment, where you must be confident about what you've learned through the week, and even search and learn further on your own. That's how you learn things!

I have learned every concept of Linear Algebra needed for Machine Learning and Data Science and Deep Learning and all other fields included in Artificial Intelligence. With theoretical knowledge and practical implementation using Python's NumPy library, I feel very confident and ready to dive deep into the fields of Machine Learning and Deep Learning.

The course is really nice! I enjoyed it! One thing I may recommend from my side is to add more explanation videos for week 4 (the one with eigenvectors and eigenvalues). Understanding the topics involved and their real-life application is far from intuitive. And completing the quizzes/labs was more difficult than for other weeks

The course started from the very basics so it was really easy to follow along . The content was structured into very short videos and quizzes therefore its very convenient to follow with a busy schedule. Finally, I would recommend this for anyone interested in machine learning with the self paced environment its perfect.

This is a good course. I recommend it. However, warning: If you do not know any Python you may struggle to complete the programming assignments. I recommend taking some Python and Numpy courses before (or while) taking this course. You are going to need it anyway if you want to study Machine Learning and Data Science.

Although I prefer rigorous mathematics, I still find this course amazing at demonstrating the application of linear algebra in machine learning. As for me, the most exciting part of the course is the principal component analysis - It is awesome to see an application of the principal axis theorem for the first time.

Apart from Python Assignments (Week 3 and 4 are very difficult), This course is really a Friendly introduction to Linear Algebra, which gives a basic and concrete understanding of how linear Algebra is used in Machine Learning. This basically helps the learner how to interpret the data in the form of Math.

This was basically a revision for me, but I really enjoyed the course, especially the way Mr. Luis Serrano is approaching and visualising the concepts. It's also nice to always relate each concept to real-world application. Learning Maths this way is much more funny and easy to understand.

I have discovered many things about linear algebra with this course. The lectures were good and clear, the exams were not too difficult and I was able to finish this course at my own pace. I recommend this course especially for those who want to know more about linear regression.

This is a great course that balances linear algebra theory and application. Prof. Luis Serrano excels at building intuition about the various elements and methods in the course through excellent visualizations and clear exercises. Additionally, the content is very well organized.

Very informative, this gives a good understanding of the concepts of matrices and vectors and why it is important in machine learning. This course ends with the concept of PCA, a popular technique used in ML to transform high-dimensional data to low-dimensional data. Thanks!!

The course material on Coursera is excellent. Delving into the concepts and utilizing the provided resources for further research significantly enhances one's understanding of the topics. This comprehensive learning experience truly enriches the overall educational journey.

Perfect pace and level of difficulty. Coding assignments are really well structured. Material shared is easy to be used as a reference in future. Time was well spent in refreshing my linear algebra knowledge and learning about how to use NumPy to handle linear algebra.