Вернуться к Machine Learning: Regression

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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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автор: Daniel V

•Jun 10, 2016

I do not this regression :D

Honestly, I can not thank Carlos and Emily enough given such a solid and casual understanding.

I am looking forward to every new Session and ofcourse the Capstone. But still a long way there, which is good news, more time with Carlos and Emily... yeah!

автор: Andre J

•Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

автор: Richard N B A

•Feb 02, 2016

Great course! Not simply a machine learning black box tutorial - like a few courses out there - but delves into the mathematics behind the algorithms (with several optional, more advanced excursions provided) and requires that we actually implement a few of the ideas ourselves.

автор: Freddie S

•Jul 25, 2016

Excellent combination of conceptual and practical quizzes. Providing the presentation slides is a great note-taking aid, as well as use of "ride-along" notebooks. The progressive use of the same dataset throughout the modules greatly aided focus on learning the algorithms.

автор: Nihal T

•Sep 25, 2017

Great course to get started in the Machine learning , it covers each and every concept of Regression . All the concepts are explained in so simple way that even a high school kid wont have any trouble understanding Machine learning . I would highly recommend taking this.

автор: Rohit G

•Dec 29, 2015

Absolutely loved the way Emily tackled the course content - the knowledge I gained regarding LASSO & RIdge was something even I wasn't expecting. Also the optional video helped a lot to understand the mathematics better as compared to a mechanical write-down of the steps

автор: Suresh A

•Apr 19, 2016

Fantastic course.

Lot of courses I have taken do not give the mathematical formulation. This course provides a detailed understanding of the math behind ML.

Also in the programming exercises one implements the algorithms from scratch and also use the existing libraries.

автор: Mohamed A

•May 01, 2016

Great course! all materials are well structured and introduce each concept concisely. I enjoyed all programming assignments. Take this course if you would like to know more about regression rather than simply finding the perfect hyper-plane that approximate your data.

автор: Victor C

•May 28, 2017

Emily Fox is exceptional. It's a smooth airplane ride through often turbulent paths. That's harder to do than it might seem as most teachers get mired in details that confuse and/or distract the student. I would think that any course she teaches is worth taking.

автор: Abhinav U

•Jan 11, 2016

Great course, very detailed and hands on, also including appropriate amount of mathematical rigour to help you understand what is going on under the hood. Highly recommended. I specially liked the modules on Ridge regression and Lasso regression, really well done.

автор: Dan L

•Dec 28, 2015

I found this an excellent introduction to the topic with a good mix of well-presented material and practical application using the IPython notebooks. I would love to have the course finish with a project where we apply the learned methods to a different data set.

автор: 张明

•Dec 04, 2015

Very responsible teachers and practical classes content.You can not only learning the ML theory from scratch,but also learn to implement the algorithm using python by yourself.This is the best ML course I ever seen.

Thanks for the teachers' hard-work.You are great!

автор: Muhammad U C

•Feb 12, 2016

Excellent. This is an ideal course in order to understand various aspects of regression techniques. Explanation using hands-on exercises helps me learn this course very effectively. I must appreciate the efforts of both Instructors (Prof. Emily & Prof. Carlos).

автор: Amal G

•Sep 10, 2016

I felt that the course was detailed and contained significant in-depth study about regression techniques. The assignments were well designed, starting from a single step and eventually enabling the candidate to be able to write the complete methods on his own.

автор: Lech G

•Jan 06, 2016

This is probably the best Coursera course I have completed so far (and I am kind of Coursera junkie). very well structured, the right amount of math and driven by the experiments on the real data.

Looking forward to Classification course and others in series.

автор: Fan D

•Jan 03, 2017

The regression is done very well. I love the tutorials especially, they are very clear with good test feedbacks on some of the latter week contents. If you want to get into machine learning, this is a very important part to help you with all the other parts.

автор: Igor P

•Feb 26, 2016

I liked pretty much all of the content.

The lectures are detailed.

The assignments helped me understand the techniques used in regression. The step by step approach is great.

What I dislike a bit is the promotion of proprietary and expensive Graphlab software.

автор: Cal D

•Dec 19, 2015

A few minor glitches with the homework assignments so far. Hopefully this is only because it is the first time the class is being offered.

I love the instructors. Great enthusiasm and both clearly love what they do. Inspiring for data scientists in training.

автор: Simng D

•Jul 09, 2018

This is a great course! The course is easily understand, the lecturers are very nicely talking in the videos to show you the knowledge of regression. The assignments are designed in a way helping you learn, practice and implement the regression algorithms.

автор: Jerry S

•Apr 02, 2017

Really exciting course. The concepts are well explained and implementing algorithms by myself is really a inspiring experience. It is really a pity that the last 2 courses in the specialization were canceled. I am even willing to pay them for 100$ each!!!!

автор: Christopher W

•Mar 28, 2016

Pretty challenging from a mathematical perspective, but extremely interesting and well-explained. I was glad to see there were plenty of opportunities to use Pandas and other Python libraries instead of just relying on Graphlab. Very happy with this class.

автор: Aviad B

•Oct 10, 2017

Excellent course. Highly recommended. Emily Fox is clear and comprehensive. In addition, this module's exercises can be fully completed using Python's Pandas sklearn and numpy libraries and without requiring the propriety GraphLab library. Good work!

автор: Dauren B

•Dec 23, 2017

Good insight into regression models. You will dive into the details of implementations of Lasso and Ridge regularization techniques. The course is actually easy to grasp for graduates with technical background, never the less gives good knowledge.

автор: Adil A

•Mar 15, 2017

Very nice course... The instructors were really great, the explanations, the presentations, even the color schemes were all really great... Definitely one of the most fun courses I've taken at Coursera... The assignments were also well designed...

автор: Filipe G

•Mar 12, 2016

The best Machine learning course I ever took. I compare it very favourably to Jeff Leek's course, or Andew Ng's course - which are both good in their own right.

A lot of effort went into making this a really good course. I very much recommend it.

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