Nov 26, 2018
Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.
Aug 04, 2019
Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.
por Tanuj J•
Jan 19, 2019
Topics need to be covered more in depth. Too much information packed into this course. Instructor's explanations are also not clear most of the time. It will be hard to follow this course if you don't have some background with calculus.
por Nushaine F•
Jul 18, 2019
This is my first time learning calculus (I'm a 16 y/o high-school sophomore), and I'm satisfied with this course. The instructors were great, and the assignments are awesome.
If I would suggest one improvement, it would be to give more examples in the lectures. Some lectures were packed with examples, and some had none at all. I had to often refer to Khan Academy and YouTube to learn the concepts which the instructors did not provide an example for. (Especially in Week 4). Sometimes this would frustrate me because it would take me hours to grasp a concept.
Having said this, this course is for you if: (1) - you want a refresher on fundamental calculus concepts that relate to machine learning, or (2) - if you want to learn calculus for the first time, and you have a strong desire to learn these concepts. But no matter what, DON'T GIVE UP and don't stop until you've completed the course.
I hoped this has helped and good luck on your ML journey!
por Valeria B•
Jun 17, 2019
The first part of the course is fine. Towards the end, lots of interesting concepts explained too quickly. I'd rather have more detailed explanations, especially about linear and non-linear regression.
The examples are quite good.
por Marc P•
Apr 28, 2019
The course is led by two instructor and my ratings is an average of the two performances. The videos in week 1 to 4 are absolutely outstanding and a pleasure to follow. The ones in week 5 and 6 are ok but not great. The use of quizzes and coding assignments throughout the course is very engaging and of great use for retention and application of the learned subjects.
Mar 31, 2019
Some errors confused many students. And they are remained unfixed.
por Jonathan C•
Oct 24, 2019
I don't want to be too hard on this course since I really liked some parts of it. Especially, the instructor in Week 1 - 4 did a good job explaining the concepts and overall one can clearly see that a lot of effort was put into the creation of this course. However, I found that a lot of topics could be handled a lot more in-depth.
The assessment at the end of a week was not really challenging and does not require a deep understanding of the concepts. Some of the quizzes were more challenging but in the assessments it was often only required to answer questions based on graphs or other images of functions. Most of the programming assignments only required the student to fill in some easier blanks.
I still do not know what the Taylor Series Chapter was about. I guess this is an important concept but I was not sure how this relates to machine learning. If you call a course Math for Machine Learning, I would expect that you relate the concepts to Machine Learning.
Maybe, it is just me but I would have been glad if this course had offered more depth and took at least double the amount of time to complete. This would have been more rewarding, as I do not feel that I learned as much as I hoped for when I started this course.
por James L T•
Nov 13, 2018
Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.
por Oleg B•
Dec 12, 2018
Excellent summaries of important points.
por Andrii S•
Jan 20, 2019
por João C L S•
Apr 17, 2019
I liked the course specially because I finally understood Backpropagation, an old frustration from Andrew Ng's Machine Learning course. It covers the main topics for Mathematics for Machine Learning as promised. Two weak points: (1) the Newton-Raphson convergence problems, superficially covered in the lectures, but has a challenging test, no forum support, no other source indicated for helping us. (2) The forum is abandoned. I've set two problems, one of them about an error in a lecture and the second about the problem with Newton-Raphson lecture. No responses from the lecturers or mentors.
por Benjamin F•
Nov 01, 2019
Relevant content. Great instructions. Likable instructors. Very bad coding assignments.
por Ong J R•
Jul 23, 2018
Course videos and quizzes are good and content is clearly explained. However, too many concepts are covered with too little depth. For example least squares and non-linear least squares involve fundamental concepts that should be covered and alone, would at least 2 weeks to teach. Lagrange multipliers and Taylor series are barely introduced with very little mathematical derivation involved. I had the impression that I would learn more mathematical theory than machine learning in this course, it didn't turn out to be so.
por Carsten H•
Mar 31, 2018
Too many derivatives of pointless functions.
por Seongwoo K•
Sep 24, 2019
This specialization consists of the courses which deliver essential mathematical background to ML learners. I think learners would feel confident and solid when diving into ML after taking this course.
Video lectures are great with clear graphics and lecturers are passionate with energy. The most outstanding part is the programming assignments: They are designed so elegantly that you can get intuition right away once you go through them. They are simply amazing.
Meanwhile, be aware that learning curves are often steep at some points. Without some basic python skill and ML knowledge, I guess quite many people would feel frustrated. But please don't give up and push it through to completion. You will be absolutely rewarded at the end.
Thank you for great contents, David and Sam.
por Eric P•
Apr 09, 2019
Challenging in places but another great speedy introduction to the relevant maths and how they are applied to ML. The best thing about this course is that you learn the general mathematical concepts and then see them in action in ML through examples and exercises. It's great. I used this course to refresh my maths skills learned long ago. I also found the pace good: neither too slow or too fast. The course would probably be quite challenging for someone who never had exposure to the concept of matrix algebra or derivatives.
por ash g•
Mar 18, 2019
I am enjoying this course massively. I am on week 5 and the lecturer has been great so far. Some of the programming assignments are a bit easy as in some cases the blanks to fill in are rather self-explanatory.
The exercise questions progress in difficulty nicely and are sized well. References to tackle more questions to solidify the understanding could be good, however I recognise that the aim is to teach the intuition and then move on and apply it in Machine Learning examples, rather than being a mathematics course alone.
por Nelson F A•
Mar 22, 2019
Very intense course. However, now that I have moved on to Andrew Ng's ML course, I am so glad I finished it. Understanding the math behind ML makes learning it so much more enjoyable. Before it was like shooting in the dark. My python code wouldn't and ML-concepts would take a lot of time and effort to sink in. Sometimes not at all... This course armed me with the tools to succeed in a career in ML and AI. Looking forward to finishing the specialization!
por Artem D•
Aug 10, 2018
I really liked the teachers and everything they prepared for the students.
Lectures are entertaining, not boring.
Assignments are interesting. Especially, i've found very useful the structure of learning: (1) you have a short lecture, (2) you have a small quiz which continue to intriduce you to the topic and in parallel let you to try it on practice - it was really great!
Thank you a lot! I loved this course (as a previous one) so much!
Jun 06, 2018
his course really meet expetation.It really help understand a lot multivariate Calculusand build me intuitions.Now i'm confident in learning ml.
The content is abundant,i really love the visualization and programming work.The programming work is fascinating,elabrated-designed,fully explained,i want more and harder programming work.
Sam is very passionate, creating a excitied study atmosphere, i really like his stress when speaking.
por Gyamfi A K•
Jul 28, 2019
I'll call this course, Multivariable calculus made easy!!! Like the first course in this specialization, the lecturers tried to appeal to my intuition. Avoiding the very precise technical presentation in the traditional multivariable calculus course. Another impressive feature is how the applications were introduced. No need for any memorization as usually required everywhere else. Thank you coursera!!!
por Arnab C•
Sep 03, 2018
I found this one to be probably one the best courses on neural network if someone is keen to learn the underlying mathematics of it. The content of the course is very concise, enough to cover the most important parts that are required to learn machine learning and just enough depth. The quizzes and assignments are of excellent qualities. Overall, I will highly recommended this course.
por J A M•
Mar 11, 2019
Excellent class! Understanding the math "under the hood" of the Python, Matlab, and R libraries is indeed the missing link holding back many data scientists from truly achieving competence and excellence. This course addresses such lacunae squarely by tackling a robust menu of relevant mathematical methods. Well done and kudos to Imperial College for taking the initiative.
por Matthias S•
May 13, 2019
The first four weeks are excellently prepared and the programming assignments are almost too easy at some points. The last two weeks and a part on backpropagation in the first four weeks give a nice intro on how to apply the learned methods. In the last two weeks there were some minor flaws in some slides and it is less easy to follow but it is still very well presented.
por Ilja S•
Nov 20, 2019
Really like the approach that a learner should get the intuition and understand how things work graphically. Then a learner should understand how numerical methods work and how math concepts can be used in Python code to do some optimization. Also, the sandpit exercises are great to easily understand how gradient descent works, which is a very important concept in ML.
por Nelson S S•
Dec 23, 2019
Muchas gracias por compartir generosamente su conocimiento.
Ha sido muy grato para mí repasar temas de cálculo multivariado, álgebra lineal y optimización.
Gracias COURSERA, Gracias MINTIC y Gracias a The Imperial College of London. Un abrazo a cada profesor que ha dado lo mejor de su enseñanza en cada uno de los videos que he observado.