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Learner Reviews & Feedback for Machine Learning: Regression by University of Washington

4.8
stars
5,286 ratings
986 reviews

About the Course

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python....

Top reviews

PD
Mar 16, 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!

KM
May 4, 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

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776 - 800 of 953 Reviews for Machine Learning: Regression

By James K

Nov 12, 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.

By Marko B

Mar 4, 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

By Michael C

Feb 28, 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.

By Jesus B P

Mar 3, 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.

By Hrushikesh M

Aug 27, 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

By Sean T

Jan 19, 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.

By Christy C

Oct 15, 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!

By Yura N

Jun 9, 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.

By Manuel T F

May 28, 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!

By Charles G

Feb 8, 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!

By Siamak S

Jan 25, 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.

By Leo B

Apr 27, 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.

By Zeph G

Jan 1, 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.

By vacous

Jul 23, 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!!!

By Tanya T

May 2, 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,

By Adrian L

Aug 9, 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.

By Conrad T

Jun 9, 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.

By Bahram A

Feb 6, 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.

By Tim J

Jan 19, 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.

By Thomas H

Dec 11, 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.

-Thomas

By Ramesh S

May 1, 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.

By Andrew T

Jan 12, 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.

By Danielle S

Dec 21, 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!).

By Marco A d S M

Sep 2, 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!

By Miguel C

Sep 6, 2017

Better than the first one, however I feel that some material was missing and that the last week was rushed. It is sad the don't talk about XGBoost and other recent algorithms for regression (Elasticnet and so on). I also think they should only focus on using Numpy/Scipy instead of closed source software they recommend.