Machine Learning: Regression

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4721 reviews

Course Description

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 dataset... 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.
Machine Learning: Regression course image
Reviews 9/10 stars
5 Reviews for Machine Learning: Regression

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Greg Hamel profile image
Greg Hamel profile image
10/10 starsCompleted
  • 116 reviews
  • 107 completed
3 years, 9 months ago
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 quiz... 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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Marcin Jankowski profile image
Marcin Jankowski profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
3 years, 5 months ago
Content: This is the second course in the 6-part Machine Learning specialization. Course covered lot of subjects. Occasionally it was challenging. Strongly recommended. Instructor. There are two instructors: Emily Fox and Carlos Guestrin. This course was mostly done by Emily. She sounds very passionate about what she does and she can pass the knowledge. Provider: Coursera is a great provider. 5-stars everytime.
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Borys Zibrov profile image
Borys Zibrov profile image
10/10 starsCompleted
  • 9 reviews
  • 8 completed
3 years, 6 months ago
Part 2 of 6 parts series from University of Washington and Dato on Machine Learning. This course is much much better than the first one because it's more challenging, more theoretical explanations are given so you can understand regression in and out. Coverage is quite good (lasso feature selection is given, kernel methods, etc.) and instructors explicitly list points that they omitted at the end so you could go and take a look. I also appreciate the choice of using either scikit-learn or sframes and dato graphlab in exercises , and I think building everything from the ground up is an excellent exercise while using dato and sframes is a very good option for those with less time (though in this part of the course you build more things from scratch, so it's almost an equal choice). Plus I like the way Emily and Carlos teach (though no Carlos in this part), the way they are engaged and excited about MOOCs. One minus for me was that the ... Part 2 of 6 parts series from University of Washington and Dato on Machine Learning. This course is much much better than the first one because it's more challenging, more theoretical explanations are given so you can understand regression in and out. Coverage is quite good (lasso feature selection is given, kernel methods, etc.) and instructors explicitly list points that they omitted at the end so you could go and take a look. I also appreciate the choice of using either scikit-learn or sframes and dato graphlab in exercises , and I think building everything from the ground up is an excellent exercise while using dato and sframes is a very good option for those with less time (though in this part of the course you build more things from scratch, so it's almost an equal choice). Plus I like the way Emily and Carlos teach (though no Carlos in this part), the way they are engaged and excited about MOOCs. One minus for me was that the forums are community driven and I didn't see any replies from professors in there. Overall, highly recommended.
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Raúl Solera profile image
Raúl Solera profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 9 months ago
Excellent introduction to regression, all concepts are introduced with a detail I have not seen in any other course. All key concepts of regression such as Ridge and Lasso regularization are perfectly explained. Other fundamental concepts of machine learning such as the bias / variance trade off are also introduced. Programming assignments are not very challenging, however the focus is in understanding regression concepts not in advancing developing skills. The bottom line: the best introduction to regression course I have found.
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Volker Hadamschek profile image
Volker Hadamschek profile image
8/10 starsTaking Now
  • 3 reviews
  • 1 completed
3 years, 9 months ago
I like this course a lot. It is much harder than course 1 of this coursera specialization which makes sense. It suprised me in a positive way that a lot of focus is on maths and on the intuition behind it which is valuable. Emily Fox is a wonderful teacher. Still I doubt how much of the content is relevant for practice. I am a kaggler and for this, there are more important techniques like boosting and so on which often get the short end of the stick in MOOCs. This seems also to be the case for the spezialization containing this course. Putting it short, I am not fully convinced in contrast to other people that we should put a lot of effort in linear regression to master machine learning. Nevertheless wonderfully presented course with depth. Recommended, especially for beginners to intermediates!
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