Вернуться к Machine Learning: Regression

4.8

stars

Оценки: 4,526

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Рецензии: 845

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!

Фильтр по:

автор: Leandro R

•May 12, 2018

This course is very good. It went above my expectations. The instructors are great and I learned a lot of Python here. I really recommend.

автор: Yashaswi P

•Sep 13, 2018

The only hindrance I had is with understanding the problem statements in assignments. It would be better to use a more unambiguous text.

автор: Md F A

•Nov 11, 2019

This is probably most in-depth Regression learning with python code, I have ever had. I liked the detail adventures of quizz questions.

автор: Hemant V G

•Mar 14, 2016

Course has covered regression in sufficient details and gave practical aspect of it. Thanks to Emily for very good content and teaching

автор: Sathiraju E

•Oct 31, 2018

It was great to take this course. Thanks to Carlos and Emily for their efforts. It's been a useful course and certainly worth my time.

автор: Harley J

•Jul 18, 2017

Very solid course for understanding machine learning principles, including developing methodical approaches to solving data problems.

автор: Joanna T L

•Mar 14, 2016

Excellent, step-by-step introduction to regression. The instructor takes her time to make sure every step is explained with details.

автор: Aayush A

•Jul 12, 2018

This course is very good.I learnt a lot from it about regression.very recommended for all trying to get expert in machine learning.

автор: Maria Z

•Dec 27, 2017

Much more difficult than the first course. It would be challenfing for those who don't have programming skills and math background.

автор: Michele P

•Aug 23, 2017

Very nice explanation of ridge and lasso regression. Assignments are easier than in Classification. I highly recommend this course!

автор: Ali A

•Mar 05, 2016

All what I can say is if there is ten stars I would have given them to this course. It is just amazing and very very very helpful.

автор: Pranas B

•Mar 19, 2016

Amazing course with good balance of visual material, practice, and optional math. Thanks Emily and Carlos, you are great teachers!

автор: Jacob M L

•Mar 02, 2016

Well presented, practical, and hands-on. By far the best Data Science / Machine Learning series I have taken thus far on Coursera.

автор: Surendar R

•Dec 23, 2018

In Depth coverage of lot of concepts, fully enjoyed it! Recommended to anyone wanting to explore in depth concepts of regression.

автор: Abe E

•Apr 28, 2017

Excellent. I used some of the videos to prepare and brush up for job interviews. Super helpful to play back at double speed ;-)

автор: Wafic E

•Nov 06, 2016

An amazing course. You can sense the effort put into the presentations and assignment work. Loving the specialization thus far.

автор: Sergio D H

•Feb 06, 2016

One of the best MOOCs I've ever tried. Great course materials and incredibly talented instructors. I can't recommend it enough.

автор: Luciano S

•Aug 07, 2017

I learned a lot of new concepts in this course. It is important to dive deeper than just understing how to use a set of tools.

автор: Rama K R N R G

•Aug 19, 2017

I really liked the progression of the topics and coverage. Good presentation with good amount of details/depth in each topic.

автор: akashkr1498

•Mar 28, 2019

please take care while framing assignment and quize question it is very difficult to understand what exactly u want us to do

автор: Evaldas B

•Nov 28, 2017

Very good and accurate course about regresion. Not just the basics but a lot of things you can use in real life chalenges.

автор: Syed A u R

•Jan 11, 2016

Exceptional course!. Emily went into great details of the regression algorithms and its application. Thoroughly enjoyed it.

автор: George G

•Oct 10, 2018

The course provided many useful insights on Regression techniques, and provided in depth understanding of the task in hand

автор: LAVSEN D

•Jul 30, 2016

A very good introduction to Machine Learning: Regression, covering the wide range of topics and explanations in lucid way.

автор: Sanjeev B

•Jan 10, 2016

Great instructors! Wish the problem sets were tougher and required more deeper thinking and choice of techniques to apply.

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