16 de mar. de 2016
I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!
4 de may. de 2020
Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the assignments...it’s just that turicreate library that caused some issues, however the course deserves a 5/5
por Martin B•
11 de abr. de 2019
Excellent explanation of the use of regression-based Machine Learning techniques. I recommend taking the specialization on Machine Learning Mathematics before taking this one - it will give you a deeper understanding of some of the mathematical concepts involved and make for a greater experience with this course. Programming assignments are good and help the learner with applying and re-visiting the material. Big drawback is the insistence in most of the assignments on using Python 2 and Graphlab Create. Workarounds for users of Pandas, Scikit-Learn, NLTK etc. are provided but it could be better.
por James K•
12 de nov. de 2016
This course covered a lot of material on linear regression. Be prepared for plenty of math (mostly algebra, a small amount of calculus). You should also be prepared to write a small-ish, if focused, amount of Python. The time commitment was higher than expected. I spent between 1-2 hours on lecture, and each programming assignment was between 2-3 hours, but overall the course is well worth the time investment. The instructors (Emily in particular for this course) are excellent, and present the lectures in an easy to follow format. I'm looking forward to the next course in the specialization.
por Marko B•
4 de mar. de 2017
In most cases offers great mathematical and logical clarity of the machine learning models. The professor does not dumb-down the concepts, but also tries hard to go through all the steps so that you understand them.
I am taking a point down for
- those few moments when it was not 100% clear, where I feel things were left out
- the practical side, there could have been more practical explanations, tips and tricks, and even more datasets than just the one house dataset that was used in each assignment
- for the usage of Graphlab Create which is not the industry standard and is not open source
por Michael C•
28 de feb. de 2016
The video lectures are particularly clear and with a good balance between intuition and details.
The assignments are interesting and they require some time even if they are not the most challenging.
Nice the choice to use Graphlab but to give detailed instructions also for Pandas and leaving the possibility open to use different software.
I will certainly take the following ones.
If you are not interested in the the full specialization and already have some exposure to python notebooks, it is possible to follow this course without the previous one.
por Jesus B P•
3 de mar. de 2020
The content of the course is great and very well explained. The only issue I had was with how it uses Turi Create vs scikit. The course is prepared to be used with Turicreate which is basically Apple dependant, I didn't manage to get it working on Windows computer. They offer more information using scikit, but in the first two lessons this is not evident, and then there is a bit of confusion on how to do part of the quizes using scikit, so I had to expend quite a lot of time to figure things out and looking for external support on the tools.
por hrushikesh m•
27 de ago. de 2020
It was a great experience while having this course I learned a lot of innovative modules. The course was swift was not too rapid and steady I would definitely recommend this course for the aspirant who is looking for a head start in the machine learning career. I might have a suggestion for the mentors that the course python notebook is not up to date and tools used to explain in the videos varies with the python the notebook this causes chaos in the user's mind and the user might get disturbed or distracted
por Sean T•
19 de ene. de 2018
Nice introduction to some core concepts and modelling techniques, enjoyed the coding exercises.
I performed exercises using pandas, lumpy, sk-learn and python 2.7 no problem. I use python 3.5 but downgraded in case it was going to cause me problems down the line, I don't think it would have now that i have completed it.
I also used the proprietary graphlab for fun in some exercises or in parallel with sklearn+pandas, is a nice library but the fact you have to register for academic license ruled it out for me.
por Christy C•
15 de oct. de 2016
Excellent course. If you want to learn the mathematical intuitions behind Lasso, Ridge and general ML concepts, this course breaks them down into details. This is the only course I've found on the many MOOC sites out there that goes into this much depth. One downside is that Coursera lacks support in general, but I do not consider it downside of this course but Coursera's. In terms of frameworks, many people like me have completed the course using Numpy and Scikit-learn instead of Dato, so it is doable!
por Yury N•
9 de jun. de 2016
Really good course. Provides basic theoretical and hands on knowledge in the regression.
Step by step programming quiz some times not demonstrate enough best practices or conceptual ideas. For example I would expect that if at step 1 we asked to assess performance based on single feature then at step 2 based on several features and compare results. But in most of the cases such comparison not proposed and results not explained. In addition it is not always clear why prediction accuracy better or worse.
por Manuel T F•
28 de may. de 2017
Great course! To be very honest it was a challenge and you made me learn a lot. Wait a second, that was the goal, right?
A couple of times I couldn't know why my answers in the quizzes were wrong. Besides, in general I found the level of the programming asignments quite fine.
Things you are doing right:
+ Tests in the programming assignments to ensure we are coding correctly
+ Constructive approach
Things you can improve:
- I can't think of something right now, so I guess it is indeed a great course!
por Charles G•
8 de feb. de 2016
Great course! Good balance of theory and practical application. I'm glad that we didn't use GraphLab as much as in the first course and more exercises were implementing the algorithms.
My one recommendation for improvement would be to revise some of the assignments with an eye for making the instructions more clear. There were a few--particularly week 5--where I understood the concepts, but it was very unclear what exactly we were being asked to do.
Looking forward to the next course!
por Siamak S•
25 de ene. de 2016
This course touches on basic concepts quite nicely and should help students with adequate math background to gain a good understanding of regression on both high and low level.
A text book and optional exercises could help attain better theoretical ground for regression. In general, references and suggested reading are missing from this specialization.
I would also like to see optional programming assignments on publicly available data sets other than the repeatedly used house prices data.
por Leo B•
27 de abr. de 2017
The material in this course is very interesting. I feel comfortable with the concepts and algorithms. I am definitely prepared to utilize these skills in an entry-level manner - it will take some hands-on practice with real datasets to build expertise, understand the nuances of these approaches and expand my knowledge base. I recommend a decent level of comfort with programming. I completed the Python for Everybody specialization, but still struggled with the programming in this course.
por Zeph G•
1 de ene. de 2016
This gives a nice survey of the techniques and approaches of Linear Regression. Lectures are structured well, and mathematical derivations are provided as optional lectures. Each week has a quiz that goes over the material, as well as programming assignments that are meant to provide a higher level understanding via Dato's GraphLab Create, as well as lower level understanding with Numpy.
I am dropping a star because some portions of the programming exercises seemed to be contrived.
23 de jul. de 2017
The material is very good and well explained into details.
However, doing the coding quiz could be kind of frustrating, as there is nothing provided for debugging. Before actually doing the quiz, there is no way that you can know if your code is completely correct. And for those who chose to use sklearn instead of graphlab, there could be some unnecessary struggle in the coding assignments.
Overall, I really appreciate the well organized content in the lessons. Good work!!!
por Tanya T•
2 de may. de 2020
The course content is very good, well described and easy to follow (with a bit of concentration!). However, given I was coding in sklearn I found that the time it took me to code and run through demos took me a significantly longer time. On one week I spent an entire 8 hours coding to finish that week alone. further support could be made available for those coding in other languages and responses to forum questions would be great,
por Adrian L•
9 de ago. de 2020
A very detailed approach for beginners (like me) to understand what is under the bonnet or behind scenes the already popular and developed Regression models available in most of the python ML libraries.
Takes you into a deep (i my opinion) and understandable journey into the Regression world, it statistical reason and explanations on why do the models do what they do and how can we optimize it results based on our targets.
por Conrad T•
9 de jun. de 2016
This was a deep dive into all things regression and I guess having a background in mathematics helped out a lot with following the material. However, I wish there was a better distribution of quizzes and assignments throughout the weekly lessons because they seemed to all come during the end of the lesson. All in all this was a very good course and I wish that I would have taken a similar course during my undergrad.
por Bahram A•
6 de feb. de 2021
I give this course a 4-start rate; it was a very informative and well-structured course. The only downside of this course is that they used some proprietary library, which has changed significantly since they used it. However, they updated the documents and added some tips on achieving the same result with open-source modules such as Pandas and NumPy. Finally, week 5 wasn't as good as the rest of the program.
por Tim J•
19 de ene. de 2016
An excellent overview of regression techniques in Machine Learning, with a very well thought-out balance between explaining concepts, providing enough maths to support the concepts (even with some optional "deep dive" lessons). For those interested in the really technical details, I think this course is an excellent start to get a grip on the concepts before diving into formal proofs. Highly recommended.
por Thomas H•
11 de dic. de 2015
Really enjoyed the course - I did well, but this is really in-depth material. I feel like it would be really difficult to implement an ML regression algorithm from scratch in a job material.
I would like to see more interactivity in the lectures (short-quizzes interspersed in the videos) in addition to the long programming assignments at the end of the course.
por Ramesh S•
1 de may. de 2018
Ridge regression could have been explained better. The best explanation was for kNN; perhaps this could have been the first module; since it is so disjointed from the others anyway.
The main reason I am rating this lower than 5 is because the notebooks for the assignments were only Graphlab based. Please do consider also giving notebooks that use pandas/numpy only.
por Andrew T•
12 de ene. de 2016
This was much more in-depth than the intro course in the sequence, which was exactly what I was hoping for.
I still think that it could stand to be more challenging. Perhaps the instructors might offer some optional, more challenging exercises. Or, maybe students could choose an alternate "challenge version" of the homework that contains fewer hints.
por Danielle S•
21 de dic. de 2015
Wonderful lectures and good assignments. Very, very clear presentations.
Minor drawbacks: there's no assistance available for the assignments (which can be quite difficult). The quizzes require sometimes information that is not directly available in the video lectures.
Note that it takes more hrs per week than mentioned (but it's worthwhile!).
por Marco A•
2 de sep. de 2017
It is interesting to understand how the gradient descent and other optimization algorithms works but it took al lot of time. In my opinion, that time could be used with another examples and practicals applications and even programming practical algorithms. After all, the course is very good produced! I will recommend it! Congratulations!