Вернуться к Machine Learning: Regression

4.8

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Оценки: 5,259

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Рецензии: 982

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....

PD

16 мар. 2016 г.

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

4 мая 2020 г.

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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автор: Rahul B

•6 февр. 2016 г.

Can't praise enough!

This IS THE COURSE for regression. Cannot believe I didn't stumble upon it earlier enough.

Great Specialization, Great Course, Great Professors, Great thought provoking Quizzes and assignments, helpful mentors and more important that any of the above, amazing comprehensive content covering each and every topic from simple linear regression to feature selection and going all the way to kNN Regression.

An enjoyable and great learning experience.

Hope to carry on the same level of enthusiasm through the rest of the specialization.

Thank You Emily, Carlos, Johan (mentor) and rest of my classmates. And of course, Thank You Coursera.

автор: Edward F

•25 июня 2017 г.

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

автор: Benoit P

•29 дек. 2016 г.

This whole specialization is an outstanding program: the instructors are entertaining, and they strike the right balance between theory and practice. Even though I consider myself quite literate in statistics and numerical optimization, I learned several new techniques that I was able to directly apply in various part of my job. We really go in depth: while other classes I've taken limit themselves to an inventory of available techniques, in this specialization I get to implement key techniques from scratch. Highly, highly recommended.

FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

автор: Toby P

•17 янв. 2016 г.

A very challenging course, but one that provides you great insights. This class focuses on the algorithms of regression and various optimization techniques. The iPython (Jupyter) notebooks lead one though solving the various problems, and luckily I have enough Python background and sufficient mathematical intuition to complete the course. Those without either would probably find the course extremely difficult, but not impossible.

I'm leaving the course with an excellent understand of how regression works, and how I can use Lasso and Ridge regressions to improve my predictions. This class is one of the best that I've taken on Coursera.

автор: Ed M

•20 февр. 2016 г.

The course is well paced, well organized, and employs a rigorous curriculum design that builds idea upon idea. Issues are well motivated and those motivations are used consistently throughout. Unlike other courses I have taken that attempt a survey across a number of areas, this course dives deep into the important topics in regression. The reward is a more thorough understanding of the theory, mathematics, utility, trade offs and implementation details of various algorithms. Moreover, the individual ideas are tied together in an overarching machine learning work flow that applies to many areas and tasks in machine learning.

автор: Hans H

•11 июня 2018 г.

Alot of information and math, great pragmatic approach in this course. I´m a Little dissapointed that the Multivariate outputs Y where and as I understood is not covered in this Courses, I would like to use that at work to predict several values. The quiz questions was a Little bit tricky, I misunderstood the questions and answered on the wrong data set. Overall the course was not easy and I needed to put in alot of hours to get the coding correct, so be ready to put down some hard work (if you are a slow Learner like me :) ). Anyway great course! And I will jump directly to the next course with Emliy and Carlos.

автор: Roger S

•12 февр. 2016 г.

Very, very good course, high-quality teaching, and well-developed quizzes and assignments. The course is challenging enough to feel motivated and satisfied when finishing a week's work, but not so challenging as to scare people (or me, at least) away. I hope (and trust) that you keep up the good work in the courses to come. If there was one thing I could change, it would be to include a discussion of statistical significance/inference (as suggested by Emily towards the end). It is unclear now how that can be analyzed in the Graphlab environment, even though it is a very important topic for practical purposes.

автор: courage s

•11 сент. 2018 г.

Emily Foxx's teaching methods in this course are the bomb. She does not give you code hints as Carlos Guestrin would, but rest assured she breaks the concepts down to basic learning blocks and does a pretty neat job at connecting the dots between blocks to present a holistic picture of the course.

I called out her name countless times trying to wade through the programming tasks. Guess that worked for me many times as I imagined her tutoring me in a PhD class and breathing down my neck to meet deadline on pay resit fees (akin to Coursera subscription charges).

Overall, 7-Star Course and Teaching Methods.

автор: Lu E

•21 окт. 2017 г.

I loved this specialization very much !!! Emily and Calors are always very passionate and humor. In this regression course, I have learned a lot of algorithms, which make me understand how the regression functions in the first course( Machine Learning Foundations: A Case Study Approach ) work. Especially, I could contruct a function now by myself. It is really really exciting !!! Emily makes a good job to do some visiualization to make the algorithms comprehensible. But this course is kind of difficult for me and sometimes I need to watch a video so many times to understand an algorithm.

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автор: Happy-Learner

•16 янв. 2016 г.

I saw a number of machine courses that are with too general contents and more like conference presentations. It's hard to learn and grasp something from them. However this is a real Machine Course that provides informative, appropriate details and derivations from which I can learn and understand the meaning and insights buried in math symbols and equations. No doubt, the optional video lectures are excellent enhanced "nutrition." Looking forward to the three courses in this specialization. Thanks, Profs. Emily Fox and Carlos Guestrin, for instructing such wonderful authentic courses.

автор: Mark C H

•3 янв. 2016 г.

Emily did a great job and presented this course in a very clear manner. I'm in the specialization primarily for the applications of regression tools and not as much for the mathematical theory. But I have to admit, I found it very helpful when Emily went into the proofs and theory behind tools such as gradient descent. She did this in a in a straightforward manner and it ultimately helped me understand the applications better. Carlos and Emily's visual 'movie' of the Lasso convergence was also extremely helpful. I'm very much looking forward to the next course in the specialization.

автор: Grace P

•7 янв. 2016 г.

This is an excellent course. The instructors are very likeable. Each module follows the same outline 1) build intuition with simple graphs 2) introduce the matrix operations geometrically with some clever graphics 3) a rigorous mathematical discussion 4) playing with the functions in an ipython notebook especially focusing on hyperparameters, 5) implementing the regression equations in your choice of programming language. As much as I love Andrew Ng's Machine Learning course, you could take this sequence instead and get more explanation with the same mathematical rigor.

автор: Paul C

•13 авг. 2016 г.

This Machine Learning class and the rest of the Machine Learning series from the University of Washington is the best material on the subject matter. What really sets this course and series apart is the case-base methodology as well as in-depth technical subject matter. Specifically, the step through coding of the algorithms provides key insight that is seriously missed in other classes even in traditional academic settings. I highly encourage the authors and other Coursera publishers to continue to publish more educational material in the same framework.

автор: Michael B

•29 февр. 2016 г.

Excellent course on the use of regression in machine learning. It does not simply stop with simple linear regression but also tackles ridge and lasso regression using Python notebooks. One big advantage for those not familiar with Python is that the Python notebooks have just enough boiler plate code to make it feasible for Python beginners but not so much that the challenge is gone. The lectures can feel rather technical at times but this, at least in my mind, enhances the course and at no point did I feel I was "drowning" in formulas.

автор: Ahmed A

•30 нояб. 2015 г.

I was only able to complete week 1 to week 3 thoroughly, and random check on other weeks due to limited time at my disposal at this moment.

In general, I found the course to be very interesting and an excellent introduction to building predictive models . Particularly , i appreciate the way mathematical formulations was explained to carry along beginners in this areas.

Nonetheless, I would suggest that the general notation slide in week 2 should include concrete data example in a table to explain the notations ie. x[j], xi[j], etc

автор: Ryan M

•12 мар. 2016 г.

I enjoyed the first course in the series, but was slightly worried the specialization would all be too focused on the GraphLab product specifically. This course is proving that Carlos and Emily intend for us to truly understand the concepts and algorithms behind machine learning. For anyone on the fence that is concerned about this, you will learn how to implement machine learning in ANY package. In fact you will learn how to do it with no package at all! Thanks so much to Carlos and Emily and the Coursera staff, this is great!

автор: Yamin A

•10 февр. 2019 г.

Excellent course that is the second in this specialization. It goes beyond the Foundations course and delves further into utilizing machine learning with regression based methods. The course also uses Python. There is some requirement that you should have some degree of familiarity with programming, although you can pick up some skills in coding in Python even if you are not familiar with it (- I wasn't familiar with Python much, although I am familiar with other languages).

Overall, highly recommended.

автор: Norman O

•12 февр. 2018 г.

This was a great course. There were a few issues I think with some of the quiz questions and some of the lecture material. However, considering how complex these concepts are, the material was very clearly conveyed overall; and the assignments were very helpful. There seem to be a number of these types of specializations available on Coursera; and they all seem really good. However, I started out with the University of Washington machine learning specialization and haven't looked back. Well done!

автор: Vaidas A

•7 февр. 2016 г.

This course is great! I had a lot of fun going through the exercises and concepts they show are really relevant. I am not sure about the level of the whole series, as it probably is more towards beginner than intermediate, but it's great to get some practice with Python and learn / brush-up / deepen knowledge in ML.

I am really looking forward to the next class - that's probably the area I would like this series to improve, the gaps between courses are just too long.

Overall great work!

Thanks!

автор: Keng-Hui W

•18 авг. 2016 г.

I'll definitely keep learning the next course.

Some people criticized about graphlab (I thought they should offer 2 versions like RStudio instead a limit-free one. Although I feel comfortable when using graphlab, I'll still use scikit-learn after finishing all courses because it is free and I just use for personally.) but you can use scikit-learn to pass this course (although you have to spend more time) , so this is not a sufficient reason to not giving 5 stars for me.

Great course.

автор: michal b

•31 дек. 2015 г.

I took and finished Andrew Ng ML course before and I though I 'now i know something about ML', after finishing this course I feel less confident and I can see how many things there are ahead to learn. Especially when it comes to relation between size of sets vs features / model / tuning parameters of model. How much different prediction you can get with the same data!

I can't wait to next part because after Andres Ng's course I started mini project using classification.

автор: Uday A

•2 апр. 2017 г.

Amazing course - the material is taught at a good pace, and with sufficient depth. The assignments are a little confusing though - between pandas and Graphlab, it gets tough to figure out what to take as reference (the iPython notebook uses Graphlab whereas the course page uses pandas/sci-kit). There are differences in language and input values for the two, and it wasn't mentioned anywhere so it took time getting used to. All in all, great course! Thanks :)

автор: Gowtham A B

•8 окт. 2020 г.

Very good course to understand the regression concepts like simple regression, multiple regression, lasso, ridge, kNN and kernel regressions. On top of that the course explained about the gradient descent and coordinate descent algorithm really well. The course is designed very well maintaining the continuity. The lecturer's pace and the explanations are very good and easy to follow. I recommend this course to anyone who wants to start learning regression.

автор: Christopher A

•17 дек. 2015 г.

Excellent. My favourite machine learning course since Andrew Ng's class. Thorough treatment. Took us from easier hand-holding to deep in the implementation details. Talked both about theoretical considerations as well as practical fine tuning. Would maybe liked to have seen a bit more talked about the problems with data that can affect model fit (multicollinearity / skew / etc) but time constraints don't allow it in an already excellently "meaty" course.

автор: Jane z

•15 янв. 2020 г.

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 :)

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