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

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 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.

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.

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.

Amazing course. Learnt a lot. All thanks to the Coursera for providing me with Financial Aid which allowed me to have full access to this course and have some real world experience of Machine Learning through the Labs. I look forward to continue with Calculus for ML.

One should enroll in this course to explore the fundamentals and practical applications of Linear Algebra, even if you received a good grade in college but never learned it. I never expected to learn Linear Algebra the way Luis Serrano teaches it in this course.

Great course for a good enough intro into some key concepts. Accompany this with 3Blue1Brown videos on YT and you will learn a lot. I particularly liked the Eigen-stuff links to PCA, helped me understand the core basis of the calculations for PCA.

Great course to work on some of the basic and necessary topics which are prerequisite to machine learning and data science. Instructor explained the topics in a simple words and visualized the topics which made the topics easy to insight.

Excellent course! It's essential to have a basic understanding of programming, especially arrays to better understand the programming part of the course. I want to upskill myself, and this course is the stepping stone in Data Science

Luis Serrano is an amazing instructor, a fantastic mathematician, and a programmer. He can explain complex concepts easily and clearly. I enjoyed every bit of the course. Thanks, Luis, Andrew, deeplearning.ai, and Coursera