Machine Learning: Regression に戻る

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853件のレビュー

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....

Mar 17, 2016

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!

Jan 27, 2016

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

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by Himadri M

•Jul 11, 2016

Well, i took a long time to complete this, because of my academics, projects and intern. Still i recently got accelerated and completed the project with 100% grades. It has been an awesome experience to learn so much concepts under a single course.

Thanks a lot to the instructors Carlos and Emily for putting up this marvelous course. :)

by Anantha P

•Aug 06, 2018

Great course on Regression. This course explains the basic regression algorithms and the math behind these algorithms in a way that is easily understandable. Apart from the explanation, the assignments are also awesome, where you get to try out all the algorithms in the machine learning libraries as well as implement them your own.

by Hanqiao L

•Mar 11, 2016

Way better than what I was taught in a regular machne learning class in university. Personaly I donot like math heavy where instructors derive whole bunch of equations. This course balances math theory and practical implementation very well. Thanks so much for making all these key comcepts and algorithms vivid and understandable.

by Dhananjay M

•Feb 08, 2016

It is an amazing course being taught by professor Emily . Being a computer science major it is very difficult to see how the statistical and mathematica algorithm we learn will be used. This course has helped me picturize the algorithm and with this case-study based approach it has helped me understand Regression really well.

by Maxence L

•Aug 10, 2016

Ce cours est une excellente opportunité d'appréhender par la pratique les concepts fondamentaux de la régression statistique, et de pouvoir les mobiliser dans une optique prédictive. Orienté sur les aspects concrets, il pourra également compléter avantageusement une formation initialement orientée sur le versant statistique.

by Asif N

•Jul 05, 2017

I love the teaching style of Emily. Her pronunciation is very clear and her short series of videos develops my interest more and more. The first course of this specialization made my interest to complete the specialization. I love the case study methodology that clarified all my confusion remained after attending the class.

by Ganesan P

•Jun 20, 2016

Very good course to get the foundations right. Emily has done an excellent job in explaining the material and she reinforces the concepts with examples. I strongly believe this course will provide the required skills to explore further topics in this area. Great Job and thanks to Coursera for providing us this platform.

by Prashant R

•Aug 08, 2016

This course is one the most brilliant courses available on machine learning. My only advise is to stick with the course even in the face of steep learning curve on some of the advanced machine learning techniques . Furthermore, completing the project using sklearn and python is bit difficult but very useful in long run.

by 朱顺

•Feb 24, 2016

The course becomes More and More deep and interesting .

The materials are not hard but need thinking. The Programming Assignments are great and give instructions how to build complex software.

I think these skills are extremely useful for our jobs to write software with the detail documents and Divide and Conquer skill.

by Omar S

•Aug 22, 2016

A great continuation to the previous course. This time the sole focus is on Regression, the instructor provides a very gradual approach to the concept. Through the assignments and the various case studies I finished the course with great knowledge of Regression and feel more comfortable now tackling regression problems

by Ilias A

•Dec 30, 2018

Wow, just wow ! This course had a great scope, digging in on the concepts / methodologies that are crucial for regression, while at the same time discussing more general and always-present concepts of a machine learning task. A learning powerhouse ! I think i must pass it a second time, to really get into the details.

by Willismar M C

•Oct 14, 2016

Amazing course, I enjoined the talking about the linear model, regularization, gradient descent in how to optimize the weights . In special I enjoyed so much the OPTIONAL videos talking more details of some aspects of machine learning like bias and variance. I am very pleased to have completed this course. Thank you.

by Mohammad A K

•Mar 12, 2016

Very practicum course, probably one of the best MOOCs course for Regression. I am from CS background and honestly speaking, I have passed hard time to catch all the concept. Nonetheless, great instructor and 5 stars for her! I believe she left no stone upturned to make the course understand for us. Thank all.

by Орлов А А

•Jul 29, 2019

I have finished this course and it was just great. Practical approach, great presentations, useful material. The course in fact require basic statistics and math, but the most fascinating thing about the course is how Carlos and Emily tend to explain really hard and cool material in very simple way. Great!

by YEH T P

•Mar 11, 2017

This is an amazing tour about regression and machine learning. You will learn basic linear regression first and dig into some practical problem include overfitting, feature selection, cross validation, this is a great course for people who interested in machine learning and have basic programming skill.

by charan S

•Jul 22, 2017

Amazing course which intuitive knowledge base. I personally liked the analysis part of every concept and algorithm via curves. This interpretation is very rare in most of the courses. Thanks for a such a beautiful course. And even the implementation via python graphLab was a good practise to learn.

by Dipanjan S

•May 15, 2016

Excellent lectures with great explanations for the concepts as well as the mathematical equations and derivations. The assignments and concrete implementations also really helped reinforce the same concepts and to get a better idea of how it can be used to solve real world problem. Really amazing!

by nick

•Nov 04, 2016

It is really a lovely course! One point, among many others, that really amazed me is the geometric explanation of ridge and lasso! For me, I think the course would be better if the content can be more mathematically challenging but it is only a suggestion and the course is already quite perfect.

by Daniel R

•Feb 07, 2016

The detail level of the regression covered in this course is absolutely necessary if you want to achieve an outstanding level in the Machine Learning area.

The teacher is awesome and the capacity of making it simple for everyone is really from another planet! The best course I have taken so far!

by Sagara P

•Jun 19, 2016

Actually implemented Gradient Descent, Ridge Regression, Lasso etc.!!! No other course out their teaches the stuff contained in this one. Each algorithm is first run using a library. I used Pandas and SciKit. Then, we implement it from scratch! So the level of knowledge you gain is compounded.

by Kowndinya V

•Apr 01, 2018

This course gives deeper understanding of regression concepts. There were insights that are really helpful esp related to interpretation of coefficients. IMO, these insights are not obvious. Provides insights into different regression choices that are available along with their pros and cons.

by Wei F

•Mar 07, 2016

I like this course a lot. Frankly speaking, this is my first completed course on Coursera. The instructor is so good that I could easily follow everything in the class. Give lots of credits to the assignments. They're very easy to follow for me. I really enjoy working on them. Thanks a lot!!!

by Yaron K

•Aug 14, 2016

Prof. Emily Fox is definitely enthusiastic, and gives clear explanations. The assignments add to the understanding of the material. While Graphlab, which is idiosyncratic is still used, explanations are given how to use Sci-Kit learn. A technical course, not only ideas - put also algorithms.

by Anindya S

•Jan 02, 2016

Dr. Carlos Guestrin and Dr. Emily Fox are amazing. Needless to say, their way of teaching is absolutely brilliant and fun to learn, concepts which took me few days to learn now takes an hour or so, this is primarily due to their mastery on the subject matter and their lucid way of teaching.

by Mohit K

•Apr 21, 2018

I found this course more useful as compared to the first one. I really like this. One suggestion here, I would like you to incorporate is that you must have given small project work at the end of this course. This course is more technical and it would be helpful if we do some live project.