An absolutely wonderful course. I spent months on end trying to finish Gil Strang's introduction to linear algebra MIT course with no avail. That's why I greatly appreciate the conciseness that this course offers.

Very well done! All the foundational math concepts are very well presented, explained, and illustrated at the appropriate pace (for me). The teacher is very clear and engaging. The Jupyter notebooks are great.

Linear Algebra for Machine Learning and Data Science is an exceptional course. With clear explanations and hands-on exercises, it's a must for anyone looking to excel in machine learning and data science.

This course is very well organised The tools and programming assignments are very instructive. I am going to continue working on a few project to consolidate my learning and continue with further courses.

An insightful course into the inner workings of some of the technology of today, like YouTube's recommendation algorithm, or Google's PageRank algorithm, and how matrices and vectors are deeply involved.

Best course ever for Machine Learning. I appreciate the efforts of all who made that course so focused. Stay blessed the whole team and HEC Pakistan for giving me such an amazing opportunity of learning.

Relatively basic. But if you have already learn Linear Algebra before, and wanna review quickly, or just wanna start to learn machine learning or deep learning. This course will be a suitable option.

I enjoyed this course I've learned how to solve systems of equations, matrices, and more. I've learned how to implement them using Python and how some machine-learning algorithms work

Great course and excellent instructor. All the concepts are explained in details with visual concept. There are also some coding labs so that we can implement what we have learnt.

While people focus on teaching how to solve problems basically, It is very good to see people speak about maths like science as a concept with good visualization!. Great work guys.

Great course. Although I studied Linear Algebra many years ago in Engineering and Physics, it was an excellent refresher. The instructor really tied things together well.

Very fun intuitive way to learn Linear Algebra. Mr. Serrano gave me an understanding of what the concepts truly mean and why which I have never had before. Thank you!

This course has given me new confidence to develop a mathematical mindset. After decades in data engineering, I am starting my new role in Machine Learning and AI.

I'll really recommend to anyone eager to acquire the basic requirements for Machine Learning and Deep Learning. It was fun to learn with very practical exercises

This class employs straightforward examples and visualizations, aiding me in grasping the key concepts more efficiently. I thoroughly enjoyed this course.

before this course, I was just in jungle by not knowing anywhere, but this course opens my eyes and it makes everything clearer at the foundational level.

Best Visual Explanation, I've got new thinking of the same things which I had learned in the Past. It great Course Thanks for making Such Amazing Content.

I appreciated this course very much, except for the sometimes inaccurate task descriptions in the programming labs.

It got decent Khan Academy vibes :)

Despite the fact I have already taken the same content at my University, It all felt really new to me, and It was like the first time I understand.

Course provides good starting point for mastering Linear Algebra. I liked very much the programming assignments provided at the end of every week.

This course was really a gem of course with various interactive labs and quizzes. I liked the labs very much and also the lecturer was top class.

Extremely solid and important for anyone looking to have a greater understanding of vector and matrix data representations in machine learning.

Great course. Explains Linear algebra in a clear and concise way, while also providing examples and exercises to further your understanding.

Luis Serrano is one of the most gifted teachers I've ever experienced and had the most grateful occasion of listening to and learning from.

A couple of minor topics in eigenvalues and eigenvectors need further explanation near the end of the course but otherwise well taught.