Chevron Left
Voltar para Machine Learning: Regression

Comentários e feedback de alunos de Machine Learning: Regression da instituição Universidade de Washington

4.8
4,176 classificações
795 avaliações

Sobre o curso

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....

Melhores avaliações

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!

Filtrar por:

1 — 25 de {totalReviews} Avaliações para o Machine Learning: Regression

por Jafed E

May 14, 2019

Able to concentrate and stay focused for periods of several hours, even when tasks are relatively mundane, and doesn't make mistakes. He has a high boredom threshold. Always assured and confident in demeanour and presentation of ideas without being aggressively over-confident. No absences without valid reason in 6 months. Reaches a decision rapidly after taking account of all likely outcomes and estimating the route most likely to bring success. The decisions almost always turn out to be good ones.

This Course always completes any assignment on time and to a high standard. This Course has outstanding artistic or craft skills, bringing creativity and originality to the task. Aiming for a top job in the organization. He sets very high standards, aware that this will bring attention and promotion. This Course pays great attention to detail. He always presented work properly checked and completely free of error.

por leonardo d

Oct 28, 2018

Excellent course, the professors made it very easy to learn quite powerful technics like gradient descend and coordinate descend. I always saw them like black-boxes, but now, thanks to this course I not only understand how they really work, but I learned how to apply them to real data. This course was simply awesome.

por Hiral P

Oct 09, 2018

I loved this course because of the detail understanding of the concepts. I was looking for a course which provide detail understanding of algorithms, and here I am. I am giving four stars for what has been given in detail, not five because I something is left ;) interpretation..

por Thuc D X

Jun 18, 2019

The program assignment's description was written badly and hard to follow

For example: in week 6's assignment, the description doesn't indicate features list but ask students to compute distance between two houses. I could only find out the feature list in provided ipython notebook template for graphlab which I apparently didn't use.

por Naman M

Jun 15, 2019

It is the best course on the coursera for machine learning

por Yufeng X

Jun 14, 2019

Best lectures!

por Giampiero M

Jun 14, 2019

great course, with more relevant technical infos

por Md s

Jun 09, 2019

Awesome Course , really helpful to do things from scratch

por Santosh K D

Jun 05, 2019

Professor Emily Fox should do a follow up for this course. It was so simple and intuitive to understand. I want to work as a PhD student under her.

por Carin N

Jun 05, 2019

The courses get better and have more assistance for those of us who can't / didn't use graph lab. It is still outdated as python 3 came out after the course was created. But did learn a lot of stuff. Module 4 was the most frustrating as you'll get the wrong answers if you use pandas/sklearn.

por Aakash S

Jun 05, 2019

Amazing Course. Thanks.

por Xi C

May 22, 2019

Very intuitive explanations!

por Vibhutesh K S

May 20, 2019

This is indeed a good course. Covering even much more than I had previously expected. The instructions were quite clear to me and the programming assignments were quite interesting.

por Oscar S

May 16, 2019

Step by Step about Regression explained well and easy to understand. Mandatory course for every data science begginer.

por Dohyoung C

May 11, 2019

Thank you for a good lecture.

The material was excellent and explanation was quite detailed and easy to understand.

Some of the programming was a little bit tricky, but I was able to pull through.

Thank you again for your efforts and I am looking forward to seeing you in the next course

por Vansh S

May 10, 2019

nice

por Nikhil P

May 01, 2019

Great course, great material

por MAO M

Apr 29, 2019

Very good for beginners

por Mukul k

Apr 22, 2019

excellent course . lots of interesting things i have learned

por Nipun G

Apr 21, 2019

Please get rid of SFrame and graphlab. However, professor is awesome!

por Gabriele P

Apr 16, 2019

The program is well structured, the lessons are interesting and the hands on nice. However, the instructor should really consider to update their material to python 3 + turicreate. Python 2 is reaching EOL in 2020 and should be avoided for teaching/training. I did most of my notebooks with python 3 and turicreate, it is really worth the effort to update the material. The tests are ok, but some looked somewhat buggy (as reported in the forum by many users) and could use a revision

por Martin B

Apr 11, 2019

Excellent explanation of the use of regression-based Machine Learning techniques. I recommend taking the specialization on Machine Learning Mathematics before taking this one - it will give you a deeper understanding of some of the mathematical concepts involved and make for a greater experience with this course. Programming assignments are good and help the learner with applying and re-visiting the material. Big drawback is the insistence in most of the assignments on using Python 2 and Graphlab Create. Workarounds for users of Pandas, Scikit-Learn, NLTK etc. are provided but it could be better.

por Ling Z

Apr 09, 2019

I took this class long time ago and just revisited it today. Compared to other online class, this class has a lot details. I am satisfied with both the clarity and depth of the content.

por Neelkanth S M

Apr 08, 2019

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

por Tahereh R

Apr 02, 2019

Thorough explanations of the essential concepts are provided! Valuable course and lectures.

Thanks!