Chevron Left
返回到 Machine Learning: Regression

學生對 华盛顿大学 提供的 Machine Learning: Regression 的評價和反饋

5,507 個評分


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....




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!



Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the’s just that turicreate library that caused some issues, however the course deserves a 5/5


51 - Machine Learning: Regression 的 75 個評論(共 988 個)

創建者 Jane z


Truly enjoyed this course! The hands-on approach is the best for deepening the understanding of the concepts and applying theories to real problems.

The 'check points', such as 'should print 0.0237082324496' ,in the jupyter notebooks are extremely valuable when other type of help is hard to obtain.

I would take classes like this in the future. Maybe, I will do a search on line to see what turn up as the closest neighbors of this course :)


創建者 Ayman K


I've studied regression and other ML concepts in so many ways, but never have I been able to understand the concepts as I did after auditing this course. I learned the following the hard way: If you want to really get an intuitive, theoretical & practical understanding of ML, you have to listen to a statistician! If I were to realize this fact earlier, I would've never jumped into ML without a degree in statistics. I do highly recommend this course.

創建者 Tsz W K


It's a truely amazing course. Having studied so much econometrics from undergraduate to PhD, I still learnt so much from this regression course. This course teaches me regressions in a way that is very different from any economics/business schools I have ever attended. While it is technically less demanding than most econometric courses from second year (UG) onwards, it is the applied/practical nature of this course that makes it so valuable.

創建者 Daniel C


Amazing - way more depth than the first course, and much narrower focus. Emily teaches all courses here and dives into the math and usage. Programming hints are given but no more walkthroughs of the code. Assignments laid out such that you need to code the algorithms correctly in order to pass assignments. Emily has an excellent way of explaining the math/calculous/reasoning behind the algorithms and proofs thereof. Love it.

創建者 Jaiyam S


This is one of the best online courses out there and not just about Machine Learning. The course was very well organized and the teaching staff was very helpful in resolving whatever issues cropped up. I would suggest you to provide additional readings/ references at the end of the course in 'Closing remarks'. Thank you Profs. Emily and Carlos for the wonderful course. Keep up the good work! I am looking forward to the next one.

創建者 Juan C A


This is an excellent course! Emily Fox does an excellent job at explaining what could be a hard concept grasp. I am talking about convex optimization and the LASSO solution. I have taken graduate level classes in convex optimization and the math is high level and can be challenging. The animation Emily presents along with the geometric intuitive explanation drives the intuition home. Thank you Emily and Carlos for this class.

創建者 Kevin K


Applications and examples are well-chosen. The choice of theory is appropriate given the audience. The problem sets are a tad on the difficult side in that extreme care must be taken to get the right answers. Some of this has to do with how the assignments are structure. Instructions need to be read several times, which can be quite tedious. In the end, they help you learn the material and force you to implement carefully.

創建者 Samuel d Z


Awesome. You need a little bit of experience but things are explained really well. So glad I took this course, I tried another one from another university, it was disastrous. It certainly helps when you know how to do programming as this takes a lot of time and can be frustrating if you are new at it. Still worth learning it this way. Would recommend to use the GraphLab and maybe later redo it with standard Python tools.

創建者 Jatin K


I have taken so many other courses for Machine Learnig already but the way this course settles down all those concepts so nicely with visualisations and examples and with hands on practice assignments and problems is remarkable.

I really wish I took this course way back as its a must take course for beginners but I am really happy after finally completing this course and making the foundations more stiff and strong

創建者 Tanmay G


Fantastic course in regression, taught with the mathematical rigor necessary to really understand (not just use) the concepts. The instructors both do an amazing job introducing the concepts piece by piece in a logical and easy to follow manner. In addition, several modules have *optional* in depth derivations of key formulae for those who want to understand the mathematical underpinnings of the regression methods

創建者 Nsair A


this course offers so much that by the time you are going through the lecture videos and the reading material, you do all the tasks along and you don't want the lecture to end. In fact by the time a lecture is finished, you want to do more and you click on the next one. the course gives a very good understanding of machine learning models and the skills gained can be used in a lot of different situations.

創建者 Pawan K S


This course is very detailed and have lot of information about regression, should be taken by anyone who wants to become master in it. But each lesson should be given a week, otherwise it becomes over whelming. Assignments are good as well, though some of them should have better instruction.

There should have been a programming assignment on kernal regression as well, as it is one of the upcoming technique.

創建者 Stephane F


Professor Fox is explaining the main algorithms (gradient / coordinate descent) in a clear and understandable way. Quite often, in blogs and reviews, Andrew Ng's course (at Stanford) is mentioned as the reference, to me it looks like these series of courses can match Ng's course on machine learning (using Octave). Being based on Python I would give the advantage to this course and recommend it.

創建者 Olexandra Z


Really great explanations for complex and important principles as well as math approaches and tools. Being a mathematician, I thought that in this math aspect there would be nothing new for me, but still it was a great refreshment and very useful explanations to understand how those methods actually work for machine learning tasks. Great balance of theory and practical applications! Thank you!

創建者 Gabor S


This a well thought out course. From the simple concepts it gradually takes you to the more complex ones. The quizzes and programming assignments help you to really understand the problems that were introduced in the videos. The video slides of every module can be downloaded as a pdf document which makes the material easily searchable. And last but not least Emily Fox is a great instructor.

創建者 Leon W


I learned quite a lot during this which I can use in practice. Everything is well explained in the video's. If I had to call a down side then I would say that I had a hard time with the math. This is because I never did something matrices and linear algebra. For those people who miss this background info I would like to say: if you're dedicated then you should be able to survive this course!

創建者 Stefan K


Very good course with detailed explanations, both great lecturers, lets you choose environment of your liking for the assignments(python and graphlab are preferred). The explanations are detailed and clear and assignments are very practical. One of the best courses and Specializations on the Coursera I have taken so far. If you contribute lot of time and effort, you will learn a lot.

創建者 Asim I


Awesome. Pure awesome. Great presentation on the theory and all the assignments force you to code solutions from scratch, you're not dependent on Graphlab. Very detailed presentation of advanced topics not covered in other superficial introductions to regression. And practical advice from the instructors shows that they are imparting practical real-world advice on running regression.

創建者 David H


Congrats to Carlos and Emily on producing a great course. As a humble software developer with no statistics background (and someone who hasn't used calculus since they left school nearly 30 years ago) I found this course to be very accessible, the concepts clearly explained, and the results of the course work have been rewarding. Thanks for kick-starting my little grey cells again.

創建者 Philippe N


I found this course really well presented and structured. II am currently developing myself a course on models and data and I have found many good ways of teaching in the presented content. The course could be even better if mentors were more responsive on the forum, as it is the case for most Coursera courses. I would encourage Coursera to take extra care of this kind of issues.

創建者 William D C


I really enjoyed this course on regression. The teaching was second to none and the course material was excellent. The assignments were relatively challenging but the slides and videos did a great job of boiling down difficult ideas into intuitive steps; they really helped! I will recommend this course to friends and colleagues interested in learning about regression and ML

創建者 Jens K


Great course that guides you to coding regression (linear, polynomial and ridge regression with gradient descend, lasso with coordinate descend, and linear-average kNN), as well as demonstrating key statistical concepts in the slides and while doing the exercises. This code deepens understanding of key regression algorithms through a hands-on, learning-by-coding approach.

創建者 Carlos F A


I already knew how to do linear regression before taking this course; however, I had always struggled to understand how ridge and lasso regression worked and what their usefulness was; thanks to this course I was finally able to understand those concepts very well. The visual explanation of how the ridge and lasso regression work made this course well worth its time.

創建者 Marcio R


This module is very rich in pratical assignments, as well as quizzes to force you to understand what you are doing. Everything is really well balanced, and all the materials are very complete. Is clear the passion from the tutors and teachers in this course. This really gives you the necessary will to proceed, and don't give up, even when things get pretty hard.

創建者 Fahad S


I thoroughly enjoyed the course and learned important machine learning concepts through it. The case study approach truly helps in building intuition for the concepts and methods we learn. Emily Ross explains complex ideas in an easy to understand intuitive manners and the visualizations are great. Looking forward to complete the rest of the specialization.