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Opiniones y comentarios de aprendices correspondientes a Machine Learning: Regression por parte de Universidad de Washington

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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....

Principales reseñas


16 de mar. de 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!


4 de may. de 2020

Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the’s just that turicreate library that caused some issues, however the course deserves a 5/5

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76 - 100 de 989 revisiones para Machine Learning: Regression

por Fahad S

31 de ene. de 2018

I thoroughly enjoyed the course and learned important machine learning concepts through it. The case study approach truly helps in building intuition for the concepts and methods we learn. Emily Ross explains complex ideas in an easy to understand intuitive manners and the visualizations are great. Looking forward to complete the rest of the specialization.

por Matthew B

4 de jun. de 2016

This is a great class! Highly recommended. Emily and Carlos are a great team. The videos are polished, the progression through the material is well organized and everything just fits together very well in this specialization. The assignments are challenging enough to be worth the effort. Great specialization... I look forward to completing every class.

por Christopher M

26 de ene. de 2019

Great course. You get to write the algorithms for OLS regressions, ridge regression, lasso regression, and for k-nearest neighbor models. The instruction even includes some optional graduate-level videos on with more detailed explanations of how more advanced algorithms for solving the regressions may be developed (eg, subgradients for lasso regression).

por Sander v d O

16 de mar. de 2016

Superb course, very well explained! The best I've taken so far!

You do need to know some Linear Algebra and Python as a prerequisite, but as a result, after hard work, I have now finally developed some true understanding of a wide range of regression algorithms.

Minor downside: i find the activity in the forum quite low, so not to useful in this course.

por Mohamed A H

27 de nov. de 2018

This course is extremely awesome!

The instructors are really professional and straight to the point. The topics are explained clearly and the assignments are crucially useful because you get to implement the concepts and algorithms in hand. Actually, you can't find a thing that's not nice about this course, at least I couldn't ;)

Very recommended!

por Theodore G

23 de oct. de 2016

A really interesting, course in the important topic of (linear) regression. The case study approach followed by the instructors makes it ideal to learn how these ideas used in real-life problems. The programming language used is Python (GraphLab Create or Open Sourced libraries), which is most probably the best choice for newcomers in the field.

por Diwakar S G

26 de jul. de 2020

This course is well organized and well designed. I was able to easily understand concepts. I would like to thank instructors for presenting content in effective way . The practice notebooks and quizzes have helped a lot in understanding concepts. I would like to thank all those people who made this course available for every student.Thank you!!

por Charlie Q

11 de ago. de 2018

Very clear and detailed presentation of concepts and techniques of the traditional regression approach that are most relevant in today's machine learning world. The assignments are well designed and may take some efforts to complete, but they are worth the time as they certainly reinforce the understanding of materials covered in the lectures.

por Joseph K

5 de dic. de 2015

I've studied machine learning quite a bit in school as well as on my own, but I wish this class was how I learned the first time around. Everything is explained so clearly and well-balanced between practical understanding vs underlying theory. Definitely serves as a good review for those of us who are looking to get back into machine learning!

por Phil O

10 de dic. de 2018

4.9 Stars really but had to round. Really enjoyable course and extremely well presented. As a working statistician/analyst this stuff hits on a lot of the import underlying logic that needs to be in your head when looking at real world projects. The 0.1 star drop is because some of the language in the questions can be confusing, an easy fix.

por Ridhwanul H

16 de oct. de 2017

Was also a great course, but personally found myself a bit confused at the last two module - lasso regression and kernel regression. Somehow managed to pass the course but I dont yet feel clear on it so I do plan on doing further studies in it, but it would be great if in future they bring in more materials for these in a much simpler way.

por Pak3d

20 de ene. de 2019

A great curse focused on understanding the mathematics of the algorithms, clearly explained and detailed. Contains "advanced" optional topics for further learning and forces you to program you own algorithms.

Do not forget to save up the results and functions programmed in previous sections, as they might be required later in the course.

por Himadri M

11 de jul. de 2016

Well, i took a long time to complete this, because of my academics, projects and intern. Still i recently got accelerated and completed the project with 100% grades. It has been an awesome experience to learn so much concepts under a single course.

Thanks a lot to the instructors Carlos and Emily for putting up this marvelous course. :)

por Anantha P

6 de ago. de 2018

Great course on Regression. This course explains the basic regression algorithms and the math behind these algorithms in a way that is easily understandable. Apart from the explanation, the assignments are also awesome, where you get to try out all the algorithms in the machine learning libraries as well as implement them your own.

por Hanqiao L

11 de mar. de 2016

Way better than what I was taught in a regular machne learning class in university. Personaly I donot like math heavy where instructors derive whole bunch of equations. This course balances math theory and practical implementation very well. Thanks so much for making all these key comcepts and algorithms vivid and understandable.

por Dhananjay M

7 de feb. de 2016

It is an amazing course being taught by professor Emily . Being a computer science major it is very difficult to see how the statistical and mathematica algorithm we learn will be used. This course has helped me picturize the algorithm and with this case-study based approach it has helped me understand Regression really well.

por Maxence L

10 de ago. de 2016

Ce cours est une excellente opportunité d'appréhender par la pratique les concepts fondamentaux de la régression statistique, et de pouvoir les mobiliser dans une optique prédictive. Orienté sur les aspects concrets, il pourra également compléter avantageusement une formation initialement orientée sur le versant statistique.

por Asif K

5 de jul. de 2017

I love the teaching style of Emily. Her pronunciation is very clear and her short series of videos develops my interest more and more. The first course of this specialization made my interest to complete the specialization. I love the case study methodology that clarified all my confusion remained after attending the class.


10 de abr. de 2020

One of the best introductory machine learning courses out there! Very well designed and taught effectively, without skimming over the theoretical and mathematical details. I loved that there was focus on both implementing the algorithms from scratch, and using pre-built libraries. One is free to use any library of choice.

por Ganesan P

20 de jun. de 2016

Very good course to get the foundations right. Emily has done an excellent job in explaining the material and she reinforces the concepts with examples. I strongly believe this course will provide the required skills to explore further topics in this area. Great Job and thanks to Coursera for providing us this platform.

por Prashant R

8 de ago. de 2016

This course is one the most brilliant courses available on machine learning. My only advise is to stick with the course even in the face of steep learning curve on some of the advanced machine learning techniques . Furthermore, completing the project using sklearn and python is bit difficult but very useful in long run.

por SHUN Z

24 de feb. de 2016

The course becomes More and More deep and interesting .

The materials are not hard but need thinking. The Programming Assignments are great and give instructions how to build complex software.

I think these skills are extremely useful for our jobs to write software with the detail documents and Divide and Conquer skill.

por Omar S

22 de ago. de 2016

A great continuation to the previous course. This time the sole focus is on Regression, the instructor provides a very gradual approach to the concept. Through the assignments and the various case studies I finished the course with great knowledge of Regression and feel more comfortable now tackling regression problems

por Ilias A

30 de dic. de 2018

Wow, just wow ! This course had a great scope, digging in on the concepts / methodologies that are crucial for regression, while at the same time discussing more general and always-present concepts of a machine learning task. A learning powerhouse ! I think i must pass it a second time, to really get into the details.

por Willismar M C

14 de oct. de 2016

Amazing course, I enjoined the talking about the linear model, regularization, gradient descent in how to optimize the weights . In special I enjoyed so much the OPTIONAL videos talking more details of some aspects of machine learning like bias and variance. I am very pleased to have completed this course. Thank you.