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 …
Instructor Details
Courses : 2
Specification: Machine Learning: Regression

53 reviews for Machine Learning: Regression
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Md s –
Awesome Course , really helpful to do things from scratch
Yufeng X –
Best lectures!
Giampiero M –
great course, with more relevant technical infos
Naman M –
It is the best course on the coursera for machine learning
Thuc D X –
The program assignment’s description was written badly and hard to follow For example: in week 6’s assignment, the description doesn’t indicate features list but ask students to compute distance between two houses. I could only find out the feature list in provided ipython notebook template for graphlab which I apparently didn’t use.
Kevin –
Clear explanation on regularization as well as bias–variance trade–off.
Patrick M d F –
Excellent trad–off between theory, algorithims and practical examples
Lucifer Z –
awesome ML course!
Jafed E –
I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand
Charlotte B –
I definitely learned a lot in this class about different techniques and ways to use regression in machine learning. I also feel like I learned a lot about how to program in Python.
Matthew K –
The course is well structured and organized; however, there is too much focus on the complex mathematical formulas and notation. The concepts are not terribly advanced but the math involvement makes it easy to get lost. The math is obviously necessary, but I just wished the lecturer had spent more time on the concepts than trying to explain what each of the subscripts, superscripts, and greek letters meant. There were many 7 minute lectures in which 5–6 minutes would be confusing math and 1–2 minutes would be actual conceptual talk. I was able to understand what was going on, but I felt it would have stuck much better if more time was spent discussing and reiterating the concepts. The math involvement could come from the coding assignments.
Sarah J –
Great course with an extraordinary teaching team, thank you
Yabin W –
The course goes into great details to clarify difficult concepts. Besides, the assignments are well designed so that students can grasp the topic step by step through practicing.
FOTSING K H C –
great
Hanna L –
Thanks for the great class!
Genyu Z –
It’s good!
VIGNESHKUMAR R –
good
Muhammad Z H –
Thanks Professor, I learnt alot
Rajib D –
I think sometimes instructor jump to some concept without explaining why
Nitin K M –
Highly recommend this course if anyone wants to truly understand the stats used behind regression. Professor Emily has taught this specialization in the best way possible. Thank you Cousera for providing such specialization online.
abhay k –
What I was trying to get at my starting stage in ML for last 2 months, this course given in 2 weeks. Thank you coursera
JAMES R P B –
Good learning materials
Lavaneesh S –
Fantastic Course, allowed me to gain insights to regression. Both the instructors like always have been excellent. Shout out to coursera for allowing me to take this course!
Rishu R –
One of the best hands on course on ML.
Parab N S –
Excellent course on Regression by University of Washington
Hany E –
Thank you!
AJAY K –
Excellent Tutorial
James R –
G r e a t !
Harsh C –
Teaches me lots of new things
Pankaj S –
Great Course.
Neemesh J –
Awesome course
Hritik K S –
Coursera is shaping me in the best version of myself through knowledge and guide. I am always be grateful of god that I found Coursera. My online teaching guru!
Velpula M K –
Good Course
Md F A –
This is probably most in–depth Regression learning with python code, I have ever had. I liked the detail adventures of quizz questions.
Cosmos D –
Good and interesting!
Raman C –
Great way to learn all the concepts involved involved in regression.
Rushikesh M N –
Detailed derivation, Loved the way they teach.
Xuening H –
So organized, so in depth, so much fun!!!
Pawan Y –
A Very good course for ML regression.
Ahmed S –
The instructors have put a lot of effort into this course and I really appreciate that but unfortunately, I was hoping that the assignments were more interactive like in the deep learning specialization and the tool used is not required at all in any job I searched for also It’s not required to use it. I learned a lot out of this course but please update the tools used in this course
Nihar K –
It was very nice and great learning experience.
Nick S –
The videos are great, well–structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit–learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.
Kritartha G –
Really great course
Mr. J –
I am giving 5 stars. Visualization of regularization is illuminating. The programming assignments are useful.
Emil K –
I love how this course goes deep into the math, yet makes it quite approachable even if you have no math skills. Emily is so good at explaining the concepts!
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 🙂 THANK YOU!!!
Deba K D –
Very good for learning
Rajeev R –
Nice introductory ML concepts to star with.
Yuhuan Z –
Great indeed, but you have to rely on the Graphlab to realize those functions. You need to figure out whether you will use Graphlab in your future studies or work.
Matthew S –
Great class!
xun y –
Very informative course. The best part is the visualization of ridge regression and lasso regression optimization. It would be great if the professor can add one final project to walk through the entire modeling process.
Israel d S R d A –
Great course very recommended
Mariano –
very hard and challeging course. learn a lot of topics.