Machine Learning: Regression(으)로 돌아가기

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909개의 리뷰

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!

May 05, 2020

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

필터링 기준:

교육 기관: Rajesh P

•Dec 30, 2015

I really got a lot of the course. The material is explained very well. The programming assignments helped further the understanding. The recap video that summarizes the entire module in 10-15 min is also very good.

교육 기관: shoubhik b

•Jan 31, 2017

Very thorough. If you are beginner this course will give you the tools to do further study by yourself. I still go back to the lectures to refresh a few concept. Really sad that course 5 and 6 won't be released :(

교육 기관: Michael H

•Sep 02, 2016

Fantastic course. Perfect balance of practice and theory. I have tried learning regression a number of times now and after doing this course I feel like I finally have a good grasp on it. Absolutely no complaints.

교육 기관: Yu I

•Aug 04, 2016

This course was super exciting! The explanation was very intuitive, using nice visualizations. The programming assignments was really practical. It would be great for machine learning newbees to learn regression.

교육 기관: Yang X

•Feb 14, 2016

Love this course! Love the flexibility of the course but if rigor is what you want, they offer mathematical rigor in optional lectures as well. Great lectures and well-designed assignments. Highly recommended

교육 기관: Ad T

•Jul 17, 2017

Great course with just the right level of detail. Had lots of fun implementing the algorithms in Python based on the instructions and all the examples helped really understand what is happening under the hood.

교육 기관: Fernando F

•Jan 12, 2016

I think this course has been very interesting. Regression is too wide for covering entirely in a course like this but it has provided me with the basic knowledge and fundamentals to keep working in the matter.

교육 기관: Stephen M

•Jan 23, 2018

I enjoyed the math and the Python exercises, which were interesting and challenging. The functions and algorithms used in the notebooks would be a good starting point for a set of Python regression classes.

교육 기관: Mohamed A M A E

•Oct 17, 2017

it is a good contant and i learn more information such as

Simple linear regression, Multiple regressionAssessing , performanceRidge , regressionFeature selection & LassoNearest , neighbor & kernel regression

교육 기관: Alfred G

•Jun 29, 2016

I strongly recommended you guys to walk through this course. It worth it! And the programming assignment is awesome. I also recommended that you can try to use sklearn + pandas + numpy to rebuild your code.

교육 기관: Virendra S S

•Aug 09, 2018

awesome course.Regression concepts are deeply covered .Be careful doing assignments .assignments are long but they are from scratch you will get to know to how machine learning algorithm actually works .

교육 기관: Syed T U S

•Jun 28, 2018

This course is amazing and cover a wide range of topics. It broadband my knowledge in the core area of machine learning. The course content and teaching style is tremendous. Thank you Coursera and UoW.

교육 기관: KYRIAKOS M

•May 05, 2020

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

교육 기관: Eftychios V

•Jun 25, 2016

An in-depth overview of the regression techniques and models. I think it went as deep into the concepts as I wanted it to go. Being a developer I found it quite understandable, and useful.

Keep it up!

교육 기관: Rafael R d S

•Nov 30, 2016

Excelent course, I highly recommend for those who are willing to learn machine learning from the basis, this module (linear regression) covered very important parts that I used to struggle to learn

교육 기관: Fede R

•Jan 02, 2017

This course is great. Things are very clearly explained. I am particularly happy because it helped me to understand many mathematical concepts. I will try not to be scared about formulas anymore.

교육 기관: Aynalem M

•Apr 26, 2020

Very informative, practical course with excellent instructors, I would recommend this course to anyone doing basic machine learning. The only issue I see is that the course can be offered in R.

교육 기관: Chia-Sheng L

•Jan 04, 2016

This course offer many aspects like graphic comparison or detail math explanation help us understand more easily what a model or method means. The teacher have great effort on material design.

교육 기관: Anuj S

•Apr 28, 2020

One of the best course on Coursera to learn about Regression with great explanations in mathematics as well as programming. Great analogy used which helps in learning much faster and longer.

교육 기관: Prashant M

•Sep 30, 2017

This was a very satisying course with the intensity and queries that challenge me and wish to learn more. I am quite excited to learn more with the new ML bug that has caught me! Liberating.

교육 기관: Wenxin X

•Mar 12, 2016

Learned a lot! Now I have been acquired a basic understanding of machine learning! Materials are not much, so it's not painful to accept. Recommended for everybody interested in this topic!

교육 기관: Gustavo K A

•Jan 08, 2016

I had the clear sense of actually learning and not just "copying & pasting" bits of code. The questions and problems are challenging enough to make you stop and think about you just learned.

교육 기관: Sergey M

•Jan 19, 2016

A very good course! Especially that scikit-learn can be used as framework for solving assignments and at the same time exercises for programming of learning algorithms from scratch. Thanks!

교육 기관: Bilkan E

•Oct 16, 2016

Incredible course!

Very good, intuitive and simple introduction to general use machine learning and optimization techniques. I am already employing techniques learned here to my daily work.

교육 기관: vivek s

•Aug 31, 2016

it's a nice course. I have learnt many new concepts. I am from information systems background and want my career towards data science. This course helped me a lot in learning new concepts.