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Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
4,287개의 평가
814개의 리뷰

강좌 소개

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....

최상위 리뷰

PD

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!

CM

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!

필터링 기준:

Machine Learning: Regression의 785개 리뷰 중 776~785

교육 기관: Monika K

May 03, 2016

I've spent a bit of time going through the Specialisation (paid for one course here) and other courses online that offer Machine Learning with Python. I looked at books too. I've come to the conclusion that it's unforgivable to teach it using graphlab (that you have to pay for after free licence expiry) when everyone else teaches scikit learn (sklearn) for good reason.The tools used on this course are also not very good.

Everyone else teaches using text editors - for a good reason, you learn how to code properly.

The lessons are also dry and there are far too many of them.

교육 기관: Ernie M

Sep 25, 2017

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.

Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.

Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.

Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.

교육 기관: Omar A C T

May 30, 2016

this was a really boring course not for the contet bu the teacher i fell bored every video because the theacher was really slow in everything tha she was showing, it is realy dificult to get focussed in the real topics when the teacher spend a lot of time explaining things at the end wont be evaluated. As an example I am not english native speaker but a had to put the playback speed to 1.50x in order to not get bored in all videos, it was really dificult to follow the teacher at the normal velocity , i just got sleep every video. and as a record i really like this topic so it is the tacher, I took the first course and it was a good experience but this one is owfull

교육 기관: Konstantin K

Jun 19, 2016

I was not aible to complete this course for free. That was very disappointing! Universities like Stanford and John Hopkins find the opportunity to offer similar courses free of charge to peoople who want to learn. From University of Washington I have expected the same. Your bad!

Best regards

Konstantin

교육 기관: Eugene K

Feb 10, 2017

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.

Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.

교육 기관: adam h

Mar 09, 2016

gets way too in-depth with the math behind regression, to the point that it deters from the learning process. was hoping to learn better methods of interpreting or enacting regression, not the inner workings of the algorithms.

assignments got overly complex with confusing instructions. there are definitely some leaps made in the assumptions of what students' python capabilities are. vague instructions caused more frustration than desire to continue learning.

will continue in the specialization, but will not hesitate to drop out if instruction continues like this.

very disappointed.

교육 기관: Ken C

Feb 04, 2017

Not happy about course 5 & 6 got cancelled.

교육 기관: William S

May 03, 2016

This course is structured around a specific and costly Python library called Dato. It is possible to do the homework without it, but it is EXTREMELY difficult to do so. If the course wasn't structured around using Dato, it would be a lot simpler and a easier to complete the assignments. Also, a lot of the mathematical notation was written in a kind of psuedo Python code that made things confusing sometimes.

교육 기관: Mats W

Dec 17, 2016

The lecturers try to keep the instructions basic and pedagogical. Pretty good. Everything in this revolves around a tool graphlab create. Not so great, I think. It is not free (you get a one year licence) and hides all the action from the user. I don't like that the course then makes me feel that I must rely on a specific product to solve problems.

교육 기관: Ehsan M

Mar 11, 2018

The teachers have a great success in developing Tori, but, the teaching is not good. The way machine learning is presented is mixed, and all over the place.

Not worth to put time on