The course takes projects focused approach to teach you machine learning by building machine learning models and projects. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code.
The instructors did well in their presentations of the material, as well as being open and welcoming of questions. I really appreciated the illustrative exercises. In particular, how they offered enough complexity to be useful, yet demonstrative of the core concepts to ML. (Brad B. from Class 20210213)
The course managed to cover the main topics of the syllabus, with the precise focus on the most difficult concepts, like PCA. The competence of the instructors, the materials, the recorded videos, the fact that we could always ask questions at any time. (Ruben M. from Class 20210213)
Perfect agenda and pace and instructors are very helpful.(Lisa R. from Class 20201123)
The course was very helpful. and Datasets that we used were very interesting to work on.(Amin N. from Class 20201123)
Both Ali and Farnoosh were very knowledgeable and were doing their best to share their knowledge with us. I loved that they were always sharing real examples either from their jobs or their Phds. I felt lucky to be in that course and to get to learn something so so interesting with such great instructors!(Beatriz O. from Class 20201123)
enjoy learning about dimension reduction methods and clustering methods in this course.(Class 20200323)
classes were well done and the overview was often insightful.(Class 20200323)
enjoy the Q&A and real interaction with instructors in the sessions.(Class 20200323)
handsout and lecturs to explain complex concepts easily understand. The instructor did a very good job explaining some fairly difficult concepts at an appropriate level, and answered questions thoroughly and clearly.(Class 20200323)
the coding exercises are very effective to help me learn. comparing and selecting methods and rationale behind tuning of hyperparameters.(Class 20200323)
For the homework, I think a short lab-type exercise with some scaffolding and a clear purpose would have been better than an instruction for us to just go out and find a dataset to try it on.(Class 20200115)
Seeing the complete process from beginning to end within an accessible environment. The course did a few examples. I would pay again for 5 more examples, especially business (fin, fraud, ecommerce, customer segmentation) and marketing data examples.(Class 20200115)
The instructor explained the content in a very effective way.(Class 20200115)
Good class examples and assignements reinforced the couse material very well.(Class 20200115)
great notebooks to learn and follow. (Pablo G. from Class 20210213)