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.

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Gregorycompleted this course and found the course difficulty to be medium.

Machine Learning: Regression is the second course in the 6-part Machine Learning specialization offered by the University of Washington on Coursera. The 6-week course builds from simple linear regression with one input feature in the first week to ridge regression, the lasso and kernel regression. Week 3 also takes a detour to discuss important machine learning topics like the bias/variance trade-off, overfitting and validation to motivate ridge and lasso regression. Like the first course in the specialization, "Regression" uses GraphLab Create, a Python package that will only run on the 64-bi…

Machine Learning: Regression is the second course in the 6-part Machine Learning specialization offered by the University of Washington on Coursera. The 6-week course builds from simple linear regression with one input feature in the first week to ridge regression, the lasso and kernel regression. Week 3 also takes a detour to discuss important machine learning topics like the bias/variance trade-off, overfitting and validation to motivate ridge and lasso regression. Like the first course in the specialization, "Regression" uses GraphLab Create, a Python package that will only run on the 64-bit version of Python 2.7. You can technically use other tools like Scikit-learn or even R to complete the course, but using GraphLab will make things much easier because all the course materials are built around it. Knowledge of basic calculus (derivatives), linear algebra and Python is recommended. Grading is based upon weekly comprehension quizzes and programming assignments.

Each week of Machine Learning: Regression tackles specific a topic related to regression in significant depth. The lectures take adequate time to build your understanding and intuition about how the techniques work and go deep enough that you could implement the algorithms presented yourself. The presentation slides are high quality and available as .pdf downloads, although the text written by the lecturer isn't particularly neat. The lecturer isn't the best orator around but she manages to explain topics well and the course takes plenty of time to cover important considerations and review key concepts at the end of each week. Overall, the pacing and organization of course materials is excellent and the presentation, while not perfect, is personable and clear.

Every lesson in "Regression" has at least one accompanying programming assignment that explores the topics covered in lecture. The assignments are contained in Jupyter (iPython) notebooks and come with all the explanatory text and support code you need to complete them. The labs walk you through implementing some key machine learning algorithms like simple linear regression, multiple linear regression with gradient descent, ridge regression, lasso with coordinate descent and k-nearest neighbors regression. The assignments are not particularity difficult as much of the code is already written for you and most tasks you have to perform are spelled out in great detail sometimes to the point where each line of code you have to write is noted in a text comment. Some may not appreciate this level of guidance but it keeps the assignments moving along at a steady pace and puts the focus on understanding machine learning concepts rather than programming skills and limits time wasted troubleshooting bugs.

Machine Learning: Regression is an excellent introduction to regression that covers several key machine learning algorithms while building understanding of fundamental machine learning concepts that extend beyond regression. If you have any interest in regression and have an environment that can run GraphLab, take this course.

I give Machine Learning: Regression 5 out of 5 stars: Excellent.

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Normancompleted this course, spending 9 hours a week on it and found the course difficulty to be hard.

This course delves into regression in a big way. You start off fairly simple, a simple linear model on some housing data (this should be pretty familiar if you took the case study class that is prerequisite to this one), and delves into the concepts at a good pace. You will be surprised by how much you can learn just by following along in the ipython notebooks' assignments. The lectures are laid out in a logical order of progression, and go at a pace that is slow enough to fully grasp the concepts. I recommend this course to anybody that wishes to learn about regression from a ML standpoint.<…

This course delves into regression in a big way. You start off fairly simple, a simple linear model on some housing data (this should be pretty familiar if you took the case study class that is prerequisite to this one), and delves into the concepts at a good pace. You will be surprised by how much you can learn just by following along in the ipython notebooks' assignments. The lectures are laid out in a logical order of progression, and go at a pace that is slow enough to fully grasp the concepts. I recommend this course to anybody that wishes to learn about regression from a ML standpoint.

A FAIR WARNING: This course felt like a legit University level course. It delves into a LOT of concepts and covers a LOT of ground (as Emily mentions in the lectures), therefore it does consume a lot of hours per week. This course will be especially difficult for you if you do not have a working knowledge of linear algebra (I'd say level I linear algebra would suffice) and calculus (up to calculus 2)

This is perhaps one of the best course which I could have taken on regression, each and every aspect was thoroughly discussed, the assignments were good, in fact the programming assignments were built with the learning part kept in mind, and not to trap the students in programming part of it

The course is heavy in comparison to other MOOCs.

God, this would have been perfect had it been in Scikit-Learn, but then again it might have been asking too much of it

Also, I suggest the people who complete the course to go to Kaggle and try to attempt a couple of questions of this technique after the completion of this course. It would definitely help you cement your understanding

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Jasoncompleted this course, spending 8 hours a week on it and found the course difficulty to be very hard.

This is one of the most informative and useful online classes I've taken to date. The material covered is detailed and applicable broadly. It is also exceptionally hard! The assignments are very challenging and extremely precise.

I struggled frequently and it ended up taking a significant amount of time, but it was extremely well worth it in the end. I'm very excited for the next class in the specialization!

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Stevecompleted this course, spending 10 hours a week on it and found the course difficulty to be hard.

Just finished the class. It's not easy and I definitely learned a lot. My only complaints might be that if you're taking this through Coursera, you're pretty much on your own if you get stuck on something. There aren't many students taking it, and there don't seem to be any mentors to answer questions. It's also one of those theoretical classes where you don't really know how to apply the concepts after you finish.

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Y.completed this course, spending 10 hours a week on it and found the course difficulty to be medium.

The course is "chapter 2"of the Machine Learning certification from this university. The start of this course was interesting. Videos are great and iPython assignements may prove difficult. But all in all I found this course much less interesting than the "Foundatins course (chapter 1 of the specialization. It looses its objectives very fast and basically what you will learn is to code "gradient descent" algorithms on and on....after listening to hours of videos that will have no use in your daily activities. Quite disappointed, especially now that I know that the GraphLab librairy used for the course is not a free package and the home company was acquired by Apple.... I intented to do the whole Machine Learning specialization of Uni of Washington on Coursera but actually..I won't.

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Wichaiditsornpon@gmail.comcompleted this course, spending 1 hours a week on it and found the course difficulty to be easy.

"Very fun" with professors are very informative and clearly explain, Course video is very great quality, Slideshow is full of color and picture that make the course not boring and also had commentary from instructors so you can read it without watching video and you will found it's also can understand with the instructors's commentary, I really want to give 10 star here.